Google Cloud Archives

Why do data professionals prefer Google Cloud?

And why should you care?

Author: Juhani Takkunen, Data Engineer, Codento

Your data engineers have the challenging job of staying one step ahead of data scientists, ensuring that data is available, trustworthy and up-to-date when needed – even if it’s not needed right now. This way, your organization’s data remains ready to be turned into actionable business value and insights, whether for ad-hoc reports or data scientists’ deep-dive investigations.

Makes sense, right? So why isn’t everyone doing this already? The simple answer: costs.

The data platform costs can be divided into infrastructure and engineering costs, which both are quite predictable: larger data volumes require more storage and compute performance and more data sources increase the need for data engineering. While storage and platform costs have generally come down with serverless solutions especially, the data engineering effort and costs can still be significant. This unfortunately often leads to valuable data remaining untapped.

In this post, I will explore why Google Cloud, particularly its analytics database BigQuery, is a top choice for data engineers and how it can help organizations overcome common data challenges. I will show how technical tools affect design decisions and why data professionals prefer certain tools and design patterns over others.

The data engineer’s choice: Google Cloud

Key features of a good data platform are security, ease of development and maintenance, and low cost. These are some of the reasons why top professionals working with vast amounts of data, such as researchers worldwide, prefer Google Cloud. According to the StackOverflow 2024 developer survey, again, some two out of three senior data engineers currently working with BigQuery or Google Cloud would want to continue using these technologies, while less than 20% would like to switch to alternatives.

Despite these statistics, many organizations still choose data tools based on what their businesspeople are accustomed to and like to use. This frequently leads to the adoption of Microsoft platforms like Azure or Power BI. While these tools are powerful in their familiarity with the business user, they may not align with the needs of data engineers who desire more flexibility and scalability. Just like the business people are allowed to determine what is the best tool for their work, so can and should the data team. Selecting the right tool for the task is vital for success, even if it means adhering to a multi-cloud environment.

Data storage: BigQuery

Google Cloud suite of services includes an incredibly scalable and cost effective serverless analytics database called BigQuery. BigQuery offers highly scalable data storage that developers can access and modify by using familiar languages such as SQL or Python, regardless of the developer’s earlier background. Not all serverless solutions come with such benefits, as for example Azure Synapse Serverless does not directly support modifying data using SQL-DML statements (INSERT, UPDATE, DELETE).

BigQuery offers benefits like high availability, unlimited storage, and scalability. Its pricing model, based on data processed rather than stored, makes it a cost-effective solution for large datasets. As storage and operations are billed separately, there is no need to ever pause the service. BigQuery can also easily be connected to any modern BI-system, such as Looker, PowerBI or Qlik.

Pricing model based on data processed, not data stored.

The most pressing challenge for many organizations is the vast amount of unstructured data, such as text, pdfs and images, that remains untapped, as many data platforms or data platform substitutes like Excel struggle to make this data accessible for analytics.

BigQuery is optimized for machine learning (ML) tasks. Google Cloud’s acceptance among data and AI enthusiasts is evident as 70% of generative AI startups rely on Google Cloud’s AI Capabilities. This staggering number proves that the people who bet their livelihoods on data and generative AI find Google Cloud’s offering and technology most appealing. BigQuery ensures data accessibility across the organization with strict access controls, empowering employees while maintaining security. It also integrates seamlessly with other Google Cloud services, creating comprehensive data pipelines.

In early 2024, the Enterprise Strategy Group (ESG) compared the cost and features of four major cloud data warehouse solutions: BigQuery, AWS Redshift, Snowflake, and Azure Databricks SQL Serverless. They interviewed users and studied cases to build a realistic model of the three-year total cost of ownership (TCO) of these data warehouse solutions. They found that BigQuery could reduce TCO by up to 54%, offering easier operation, better flexibility, and built-in compatibility with other cloud services.

BigQuery eliminates the need to manage, monitor, and secure data warehouse infrastructure, allowing teams to focus on using insights instead of managing the process. Unlike other solutions, BigQuery is fully managed, meaning there are no physical or virtual servers to handle. It optimizes storage automatically and supports AI and machine learning work.

Data pipelines: Dataflow and Dataproc

Data pipelines often start from a simple task: load data from the source and store it in a database. One could imagine that such a repeatable, simple task can be solved using something simple like a low-/no-code solution. Unfortunately, in our experience, the data sources and scenarios are so versatile that eventually each ETL (Extract-Transform-Load) tool requires at least some custom code. Exceptions are often related to authentication, data parsing, dynamic mapping, retry-mechanisms or error handling. As simple tasks grow into more complex business problems, the simplest development tools may start to restrict the data engineers and especially maintaining the hacky solutions can be a real challenge.

Based on our customer examples, data engineers typically prefer tooling that allows multiple developers to work simultaneously. Developers need to be able to run individual pipelines locally or in a sandbox environment, reuse code with functions, and deploy code using pull-requests and version control. The last part often turns out to be the most challenging, since a successful pull-request-review requires the reviewer to be able to both review and validate the change.

The main ETL tools in Google Cloud are Dataflow and Dataproc, both offer serverless ETL solutions. Dataflow and Dataproc are based on the Apache open source projects Beam and Spark, respectively. With these tools, data engineers can write reusable and testable code with popular programming languages such as Python and Java.

A lightweight, scalable data model – as a Service, if you will

BigQuery’s cost-effective pricing model and serverless nature make it an efficient and scalable tool that allows data engineers to focus on extracting insights rather than maintaining systems or managing costs. Codento, in turn, is the leading Nordic Google Cloud-focused software integrator. Our extensive Google Cloud data platform proficiency has proven that a lightweight data model built on serverless technologies like BigQuery and Python can effectively harness data from diverse sources.

Based on our earlier hands-on experiences with customers like Nordic e-commerce leader BHG and electric car charging solution pioneer, Plugit, Codento has now built an opinionated data model for our customers. Our new turnkey solution, Lightweight Data Model is scalable in terms of performance and cost, making it suitable for organizations of all sizes. The setup is pre-configured, making it ready-to-use with minimal configuration effort, typically within eight weeks from customer’s decision to proceed.

This new Data Model solution can be implemented in your existing Google Cloud environment or in a new environment, or it can also be offered as software as a Service. In the latter case, Codento manages the data platform for you in our environment. Such a turn-key solution allows you to concentrate on your business and, if you will, to continue using your existing tools on the side of the new data model.

Key takeaways:

Google Cloud’s BigQuery offers scalable, serverless data storage for datasets of any size.
According to surveys, data professionals prefer to work with Google Cloud and BigQuery.
Google Cloud services scale effortlessly with future requirements, such as data volume, machine learning tasks, automated testing and quality controls.

About the author:

Juhani Takkunen is an experienced data engineer and Python wizard. He likes building working solutions where data flows efficiently.

Stay tuned for more detailed information and examples of the use cases! If you need more information about specific scenarios or want to schedule a free workshop to explore the opportunities in your organization, feel free to reach out to us.

A fireside chat with a Codento consultant on an assignment with Telia – Key takeaways

I am Perttu Pakkanen and my interest as Codento’s talent acquisition lead is to better articulate why consulting could be a great career choice.

When potential customers ponder whether they should use our services, they usually like to see some reference cases. Why wouldn’t our potential employees think the same?

So, I had a chat with our leading cloud architect Jari Timonen. One of Jari’s recent consultancy assignments has been Telia, and specifically Telia’s programme in developing a groundbreaking cloud service, Sirius, with Codento’s team’s help.

I asked Jari to sit with me and share some of his recent reflections. More specifically, I asked Jari to help me understand his last project, the challenges he has overcome, and the skills he has gained.

This would probably also help our candidates to get a glimpse of the day-to-day work, the technical expertise they bring, and why working with us could inspire them.

So, here we go:

Jari, please tell me a bit about the project. What kind of solution have you built?

Sure. We built an entirely new solution—something that has never been done before. It revolves around 5G latency and how edge services fit into that context. We explored how to perform edge computing easily and in a way that can be maintained using appropriate technology.

There was a lot of testing, trying out different ideas, and, of course, some hiccups along the way, but we learned continuously throughout the process.

In the end, we concluded that GKE Enterprise/Anthos was the best fit for the purpose. It allows us to manage edge computing easily and distribute workloads efficiently.

We also utilize GPU capacity at the edge to run AI models.

As expected, that’s very interesting! Then, what kind of tasks have you done in the project?

I’ve done research on the technology and contributed to the architecture. I helped guide the developers, providing insights based on my experience with architectures for the edge computing platform. I was hands-on also, working, e.g., on the configuration.

GKE Enterprise/Anthos played a key role—it’s the management tool for Kubernetes clusters, so we worked a lot with Kubernetes throughout the project.

Most of my work was on higher-level decisions, and I presented these ideas upward within the organization.

Also, of course, lots of coffee drinking and doughnut eating was involved!

I’m sure there was! I also want to know a bit more about the motivational side of the work: What has been the most interesting thing you have done working for the customer? What brings enthusiasm to your workdays? And what has been the most difficult part?

The technology is really interesting as a whole. It’s a highly complex system, but in the end, it solves many problems related to management and automation. Tackling difficult challenges has been very exciting.

As mentioned earlier, no one has really done this before, so a lot of critical thinking was required to figure things out as we went along.

As you know, learning is an integral part of our organization. Thus, the final question: What have you learned?

I’ve learned a lot about edge computing and its applications in the telecom industry. It’s been very insightful to understand how these technologies can be integrated and what opportunities exist for leveraging 5G and edge computing in the future.

Thanks Jari for the short and sweet interview!

Being part of pioneering projects like this allows for both personal and professional development. I strongly feel that at Codento you can engage in work that is not only challenging but also highly impactful in many industries.

Read more about us from our career site and see if there are any suitable opportunities for you!

You can find Jari’s more technical blog about Kubernetes and edge computing here.

About the interviewee:

Jari Timonen is Codento’s Lead Cloud Architect. He has over 20 years of experience in different software development and architecture positions.

About the interviewer:

Perttu Pakkanen is responsible for talent acquisition at Codento. Perttu wants to make sure that the employees enjoy themselves at Codento because it makes his job much easier.

People, stop misusing Kubernetes!

Unless you have a viable use case like edge computing

Author: Jari Timonen, Lead Cloud Architect

No matter what color of gift paper you wrap it in, Kubernetes is complex and costly.

Initially, Google developed the predecessor of Kubernetes and named it Borg. Since then, Google has open-sourced the technology to benefit the broader community and to advance the state-of-the-art in container cluster management. And just like Google envisioned, Kubernetes has become a crucial part of modern container orchestration. Virtually everything in Google’s own environments, for example, runs as a container, managed with Kubernetes. To me, this seems like solid proof of Kubernetes’ reliability and scalability for huge corporations like Google.

But seriously, how many companies in the Nordics are Google, or even come close?

The fact, namely, remains that moving from virtual machines to containers and Kubernetes is a big investment. Therefore, this step just isn’t for most companies and organizations. I am concerned that there are countless companies whose business comes closer to “man and dog” than that of the global cloud giants, which are spending their time playing with Kubernetes.

How many companies in the Nordics are Google?

However, one viable use case for Kubernetes is emerging: edge computing. As our CTO Markku Tuomala wrote in his recent blog, edge computing – processing data closer to its source – offers big benefits in terms of latency, bandwidth, and efficiency for large industrial companies, telecom operators, and electricity providers.

Justifying Kubernetes: Edge computing

In the otherwise fast-changing world of industrial technology, edge computing has been annoyingly “just around the corner” for years. Things are about to change, however, since a number of very handy technologies from Google are making the orchestration of Kubernetes clusters more achievable. In this blog, I will share my experiences on how GKE, GKE Enterprise, and Anthos can revolutionize edge computing for industries that need very low-latency online services.

Google Kubernetes Engine (GKE) is Google’s managed Kubernetes service. It’s a robust solution for building and managing the capabilities needed for edge computing. Anthos, in turn, extends GKE to manage Kubernetes clusters across multi-cloud and hybrid environments. GKE Enterprise, the newest addition to the mix of solutions, allows Kubernetes clusters to be managed in a multitenant architecture, across clouds and on-premises environments, eliminating the need for extra servers. Google Distributed Cloud, finally, combines software and hardware to provide a fully integrated system. Such an integrated system supports edge computing scenarios, among others.

A standout feature of GKE is its team management capabilities. GKE allows the distribution of clusters and assigning specific teams to manage them. For example, team members in different locations—Pertti in Seinäjoki and Petra in Stockholm—can be given access and control from the cloud, eliminating manual interventions. This centralized control ensures all necessary tools and permissions are included in the package, simplifying operations significantly.

In edge computing scenarios, GKE offers unmatched ease of management. For example, updating a cluster can be as simple as making one change and deploying it across the network. This ease of operation is crucial for environments where Kubernetes management and updates are usually difficult. For instance, the North American Major League Baseball, uses Anthos to host applications like real-time game analytics, which need to run locally in the park for performance reasons.

Telia and Codento lead the way to Edge as a Service

Over the past two years, Codento’s team has pioneered using GKE and GKE Enterprise for edge computing. I am proud to say that we have achieved something no one else in the world has yet.

Our journey with Nordic telecom giant Telia began 2.5 years ago. Telia wanted to maximize the return on their 5G network investments beyond only speed. They also wanted to test Anthos’s capabilities in multi-cluster management.

Significant improvements in multi-cluster management.

Our joint efforts have been successful. Significant improvements in multi-cluster management reduced the time needed to run system upgrades from weeks to the minute it requires to change one configuration number. The first pilot customer is already using Telia’s platform.

Eye on the ball – Kubernetes can add or dilute value

Despite its advantages, Kubernetes is still complex and costly, often rightfully seen as a last resort. Managing multiple Kubernetes clusters is labor-intensive and expensive, so it is usually for organizations with strong technology know-how and advanced cloud environments.

Today, however, the burden of management and monitoring is much lower, allowing teams to focus on innovation and growth. GKE Enterprise, with its robust features and ease of multitenant environment management, will in my opinion be a game-changer for large industrial companies and service providers looking to harness the power of edge computing. By simplifying cluster operations and offering centralized control, GKE Enterprise enables businesses – specifically the businesses that have the needed maturity to lead modern cloud teams – to deploy and manage edge computing capabilities efficiently.

When all these prerequisites are fulfilled, Kubernetes will stop being a value destroyer that sucks time and energy and become a driver of innovation and operational excellence.

Key takeaways:

Google Kubernetes Engine (GKE), GKE Enterprise, Anthos, and Google Distributed Cloud offer a comprehensive solution for managing Kubernetes clusters across different environments.
Kubernetes has traditionally been seen as costly and complex , but these technologies make it more accessible, enabling advanced solutions like edge computing.
With GKE Enterprise, telecom players like Telia already offer their customers multitenant edge computing services based on Kubernetes clusters.

About the author:

Jari Timonen, is an experienced software professional with more than 20 years of experience in the IT field. Jari’s passion is to build bridges between the business and the technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.

Boosting Contact Center Effectiveness with AI

Conversational AI happens at competitors’ CCs while you’re busy making other plans

Author: Janne Flinck, Data & AI Lead

Working for Nordic organizations in various industries, I have gladly noted that front-runners are already deploying modern Artificial Intelligence tools to increase their contact center efficiency and customer satisfaction. In contrast, the majority are still looking for marginal improvements via tweaks in ticket handling or streamlining the edges of their onboarding processes.

“Life is what happens to you while you’re busy making other plans.” This familiar motto applies to many Nordic customer service and contact center decision-makers regarding conversational AI: It’s happening at competitors’ contact centers while you’re reading this blog.

Exceeding customer expectations while managing costs

Whether you’re a Nordic public sector entity or a private company running your business here, exceptional customer service is crucial. According to Salesforce, nearly 90% of customers today perceive the experience delivered as important as the actual products or services. Customer service leaders and marketing and sales officers face a common challenge: providing consistent, high-quality service while managing costs and resources effectively.

Nearly 90% of customers perceive the experience delivered as important as the products or services.

You want to ensure prompt, accurate responses to customers within acceptable wait times, regardless of the time of day. Simultaneously, you must balance the cost of contact center teams and onboarding new agents. You want to stay agile and be able to scale to meet the needs of growing organizations or seasonal peaks. Moreover, you want to gain insights into customer behavior and service performance to steer strategic decisions for optimizing operations and improving service quality. This is where Google’s Customer Engagement Suite comes into play.

Agents for agents

Generative and conversational AI agents are revolutionizing customer service, particularly in contact centers. Customer Engagement Suite is a collection of Google Cloud products designed to enhance contact center agent productivity, boost customer satisfaction, and reduce operational costs.

When your agent starts a call with a customer, Customer Engagement Suite provides live transcription, real-time answers to the customers’ questions, and a discussion summary. This helps the agent focus on customer interactions without worrying about taking notes. Customer Engagement Suite’s omnichannel support covers chat, SMS, VoIP, and video, ensuring seamless customer experiences across all channels.

Generative AI agents produce automated answers to customers’ questions by integrating to enterprise knowledge bases and other internal and external data sources. Customer Engagement Suite can also automate tasks like checking order status or updating payment details, ensuring customers always receive up-to-date information and services tailored to their needs. All this increases the efficiency of operations, and we have seen customers reduce call durations by up to 10%, yielding a significant payback to the system investment.

We have seen up to 10% reductions in call duration.

Quick access to relevant agent data will also shorten the time needed for new employee onboarding. When newcomers have speedier access to appropriate knowledge, the onboarding period can be up to 25% shorter, leading to a stark improvement in efficiency.

Many customer service calls involve tedious information-seeking, often for questions that repeat over time. Customer Engagement Suite’s virtual agent chatbots can relieve your agents of the repetitive burden by automatically finding answers to common questions using existing information sources and handling text, voice, and images in customer encounters. By reducing the need for human intervention in routine cases, the chatbots free human service agents to offer a more personal and richer interaction that increases customer and employee satisfaction.

Customer Engagement Suite offers powerful analytics tools that provide insights into customer interactions. These tools help your organization identify trends, improve processes, and make data-driven decisions.

As the Customer Engagement Suite is fully developed and managed by Google, it allows you to concentrate on extracting value for your operations. The deployments are efficient due to its seamless integration with telephony and contact center applications and tools for building custom features that adapt to your processes.

The Quantified Impact of AI in Contact Centers

In the bigger picture, AI will affect both new hires and existing employees in the coming years—in both negative and positive ways, depending on your position. In Metrigy’s AI for Business Success 2024-25 global research study of 697 companies, the following was discovered:

New hires – More than half of companies were able to reduce the number of new agents they needed to hire. The numbers are substantial: Those who did not use AI in their contact center, had to hire almost twice the number of agents during the year 2023 compared to those who used AI.
Existing employees – When contact centers were augmented with AI, nearly 40% of companies were able to reduce their headcount, with the average reduction being about one in every four employees.

For business leaders looking for technology to drive cost efficiencies, AI is doing its job. For example, with the addition of AI agent assist, the average handle time dropped by an average of 30%. At the same time, each supervisor saves nearly two hours per week when AI helps with scheduling and capacity planning. In addition to making agents and supervisors more efficient, AI-enabled self-service also helps automate customer interactions so that fewer of them even require live agent attention.

Real-world success stories in the making

I am honored to help several of our leading customers in the Nordics embrace the benefits of generative AI and conversational AI in their contact center operations. The most value can be extracted in organizations where the number of daily contacts is high, and the onboarding cost is noticeable due to complex product structures. Such fields include retail, travel and leisure, banking, and insurance. Similarly, organizations with high peak demand, such as nonprofits with surging inquiries during a fundraising campaign or public offices with specific deadlines for citizens’ input, could benefit from Customer Engagement Suite. It helps diminish the burden of agents on duty, channels routine questions directly to virtual agents, and makes onboarding seasonal employees more straightforward.

As an experienced and awarded Google Cloud Solutions integrator, Codento offers comprehensive support to ensure a smooth transition to your contact center’s era of AI. The fact that Customer Engagement Suite is a complete solution developed and managed by Google will ensure a robust platform integrated with all your relevant data sources and a foreseeable future roadmap on which to build your contact center success.

Key takeaways:

The experience delivered, e.g., by your contact center agents is as important for your business as the product or service you actually sell
Google has packaged Artificial Intelligence tools for excellent customer service into a managed solution called Customer Engagement Suite
The efficiency effect of AI in Contact Centers has already been quantified and, e.g., handling times have been seen to drop by 30%
Codento is already working with Nordic organizations to harness AI for better customer experience and more efficient Contact Center operations

About the author:

About the author: Janne Flinck is an AI & Data Lead at Codento. Janne joined Codento from Accenture 2022 with extensive experience in Google Cloud Platform, Data Science, and Data Engineering. His interests are in creating and architecting data-intensive applications and tooling. Janne has three professional certifications in Google Cloud and a Master’s Degree in Economics.

Living on the Edge – Google Kubernetes Engine makes edge computing finally real

Living on the Edge

Google Kubernetes Engine makes edge computing finally real

Author: Markku Tuomala, CTO

Edge computing has been an unkept promise of 5G networks for years. Industrial companies, energy and utilities, and transportation and logistics businesses have been longing for low-latency services that would allow them to monitor and react in real-time to happenings on the field. Telecom operators, in turn, have dreamt of a genuinely novel business case for their 5G network investments, in which they would offer a scalable, cost-effective edge computing solution as a service to their customers.

Google Cloud’s packaged tools enable Edge as a Service

Edge computing is the practice of processing data closer to the source rather than relying solely on centralized cloud data centers. It offers a range of practical benefits, such as reducing latency, enhancing real-time data processing, and improving system performance. The most mentioned use cases of edge computing are real-time monitoring and control of manufacturing processes, automation of production lines, fleet management, and employee safety.

Two concepts are essential to understanding the hurdles that have been blocking the widespread use of edge computing: containerization and Kubernetes. Containerization involves packaging an application and all its dependencies into a lightweight, portable unit called a container. This allows the application to run consistently across different devices, making it ideal for deployment on edge devices with limited computing capacity. Kubernetes, in turn, acts as a management system for these containers, orchestrating their deployment, scaling, and operation to ensure they run smoothly and efficiently. Jointly, containerization and Kubernetes enable efficient, scalable, and reliable edge computing by ensuring applications can be easily deployed and managed across numerous edge locations.

Managing containerized applications with Kubernetes is a complex technological endeavor that has been a showstopper for many interesting edge computing use cases until recently. In late 2023, however, Google launched a managed service called Google Kubernetes Engine (GKE) Enterprise that will revolutionize the opportunities to offer and deploy edge computing.

Google Kubernetes Engine Enterprise for multitenant edge computing

GKE Enterprise is a tool for managing multitenant edge environments where you can cost-effectively and safely offer computing capacity from the edge for several users. These users can be the manufacturing sites of a single corporation in the same geographical area or a group of clients of a telecom operator or water or electricity company. By using GKE Enterprise, companies can efficiently manage workloads across cloud and edge environments, ensuring seamless operation and high safety availability of applications that require extremely short latency.

Chicken and egg: are use cases awaiting the technology or vice versa?

Some have claimed that edge computing is a fad, as the network connections with 30 – 60 ms latencies in the Nordics, especially, are supposedly enough for 90% of the use cases. The ambitious goal of edge computing to diminish the latency to less than ten milliseconds. This will enable some examples described above, which cannot be realized over the current networks. From my experience, I am convinced that when the appropriately priced chicken is available, the application eggs will follow in numbers. In other words, when the cost of the mature platform technology is on the right level, the game-changing use cases and applications will follow.

Aiming at <10 millisecond latencies

We at Codento have talked with more than a hundred organizations about their plans and aspirations for using artificial intelligence. Customers have delightfully novel ideas for using video surveillance connected to AI, e.g., for identifying the crossing paths of an autonomous forklift and a maintenance worker. With real-time video and a predicting AI solution, a system could reach the upcoming incident faster than a human can, potentially saving the worker’s life.

Last week, we were thrilled to introduce our first customer case in this area to the world. Telecom operator Telia and Codento have collaborated to make edge computing available to Nordic organizations through Telia’s Sirius innovation platform, with ferry operator Finferries being the first customer to pilot the service.

Edge computing transforms industries by enabling secure, low-latency, real-time data processing. For Nordic telecom operators and industrial companies, Google Kubernetes Engine Enterprise offers a powerful platform to harness its benefits.

Codento’s expert team has extensive experience with industrial customers’ businesses and processes, in-depth understanding of the AI-related use cases that Nordic companies are investigating, and awarded capabilities in Google Cloud technologies. We are eager and prepared to help your organization fully utilize edge computing and its applications. Be it a solution you want to build for your use or a platform you want to offer as a service to your customers, we are here to help.

Key takeaways:

Edge computing will enable novel use cases like video monitoring and real-time reactions to events in, e.g., industrial processes
Google Kubernetes Engine Enterprise is a solution enabling multitenant edge computing environments, adding scalability, cost, and security to “Edge as a Service”
Codento can help industrial corporations or telecom, water or electricity companies to build use cases and services based on edge computing

About the author:

Markku Tuomala, CTO,  joined Codento in 2021. Markku has 25 years of experience in software development and cloud from Elisa, the leading telecom operator in Finland. Markku was responsible for Telco and IT services cloudification strategy and was a member of Elisa’s production management team. Key tasks included Elisa software strategy and operational services setup for business critical IT outsourcing. Markku drove customer oriented development and was instrumental in business growth to Elisa Viihde, Kirja, Lompakko, Self Services and Network automation. Markku also led Elisa data center operations transformation to DevOps.

Breathe New Life into Cornerstone Systems

Take your Salesforce, SAP, Power BI, Oracle, AWS, and VMware solutions to the next level with Google Cloud

Author: Anthony Gyursanszky, CEO

We all want AI and analytics to boost our business and enable growth, but few of us have the deep pockets needed to redo our entire IT environment.

Most Nordic organizations have invested significantly in leading technologies like Salesforce, SAP, Microsoft Power BI, Oracle, AWS, and VMware. However, the jungle of AI capabilities is scattered and a coherent AI roadmap is difficult to envision.

Integrating Google Cloud with the technologies mentioned above, allows you to unlock new synergies and use advanced AI capabilities without extensive reconfiguration or additional capital expenditure.

Turbo boost your current system environment without overlapping investments

Adding Google Cloud to your IT strategy does not necessarily mean replacing existing systems. Instead, you can compliment them, enabling them to work together more effectively and deliver greater value with minimal disruption.

For example, Google Kubernetes Engine (GKE) Enterprise enables seamless deployment and management of your existing applications across hybrid and multi-cloud environments. Your Salesforce, SAP, Oracle, and VMware systems can work together more efficiently, with Google Cloud as the glue between them. The result is a more streamlined, agile IT environment that enhances the capabilities of your current investments.

Google Cloud VMware Engine, in turn, allows you to extend your existing VMware environments to Google Cloud without costly migrations or re-architecting. This enables your business to tap into Google Cloud’s vast computing and storage resources, advanced AI tools like Vertex AI machine learning platform, and robust analytics platforms like BigQuery—without a revolution in your current infrastructure.

Harness all your data and deploy the market-leading AI tools

Data-driven decision-making is crucial today for maintaining a competitive edge in any field of business. Integrating Google Cloud with, e.g., your existing Microsoft Power BI deployment will significantly enhance your analytics capabilities. Google Cloud’s BigQuery offers a robust, serverless data warehouse that can process vast amounts of data in real-time, providing deeper and faster insights than traditional analytics tools. By connecting BigQuery to Power BI, you can easily analyze data from various sources like SAP, Oracle, or Salesforce and visualize it in dashboards familiar to your end users. Such integration enables your teams to quickly draw informed conclusions based on comprehensive, up-to-date data without significant additional investment.

Furthermore, Google Cloud’s Vertex AI can integrate into your existing data workflows. This way, you can take advantage of Google’s advanced machine learning and predictive analytics tools, and the analysis results can be visualized and acted upon within Power BI.

You can also activate your SAP data with Google Cloud AI for advanced analytics and for building cutting-edge AI/ML and generative AI applications. This enhances the value of your data and positions your business to respond more swiftly to market changes.

For businesses using Oracle, Google Cloud’s Cross-Cloud Interconnect provides secure, high-performance connectivity between Google Cloud and Oracle Cloud Infrastructure (OCI). This allows you to continue leveraging Oracle’s strengths while benefiting from Google Cloud’s advanced AI, analytics, and compute capabilities—without being tied to a single vendor.

Start small, and grow compliantly as you go

One key advantage of Google Cloud is that you can start benefiting from the advanced capabilities almost immediately, driving innovation and competitive advantage with only minor incremental investments. Google Cloud’s pay-as-you-go model and flexible pricing allow you to start small, scaling up only as needed and as you gain tangible proof of the business value. This approach minimizes upfront costs while providing access to cutting-edge technologies that can accelerate your business growth.

As your business’s cloud capabilities expand, maintaining data security and compliance remains a top priority especially in the Nordic region, where regulations like GDPR are stringent. Google Cloud’s Hamina data center in Finland provides secure, EU-based infrastructure where your data stays within the region, meeting all local compliance requirements.

Google Cloud also offers advanced security features, such as Identity and Access Management (IAM), that integrate seamlessly with your existing systems like Microsoft Power BI and VMware. This ensures your data is protected across all platforms, allowing you to grow your cloud footprint securely and confidently.

Don’t put all your digital eggs in the same basket

Google Cloud’s open standards and commitment to interoperability ensure that you’re not locked into any single vendor, preserving your ability to adapt and evolve your IT strategy as needed. This strategic flexibility is crucial for businesses that want to maintain control over their IT destiny, avoiding the limitations and costs associated with vendor lock-in.

Google Cloud complements your existing IT investments and helps you gain a competitive edge from technology choices you have already made. At Codento, we specialize in helping Nordic businesses integrate Google Cloud into their IT strategies. We ensure that you can maximize the value of your current investments while positioning your business for future growth.

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

Final Episode of AI in Business Blog Series: Customer Foresight

Author: Antti Pohjolainen, Codento

In the fast-paced world of business, the ability to foresee and meet customer needs is a key differentiator between a thriving company and a struggling one. The concept of “customer foresight” revolves around the proactive anticipation of consumer demands, preferences, and behaviors. This strategic approach enables businesses to stay ahead of the curve, offering products and services that align closely with what their customers want.

Understanding Customer Needs before They Realize Them

Anticipating customer needs involves more than just offering what they ask for; it’s about understanding what they might want before they even realize it themselves. By employing various techniques, companies can gather insights, analyze trends, and predict shifts in consumer behavior, thus enabling them to tailor their offerings to align more precisely with customer expectations.

Data Analysis as the Starting Point

One of the primary methods for understanding customer needs is data analysis. Leveraging various technologies, including AI and machine learning, it is possible to find the right opportunities to pursue after, exceed customer expectations, and, perhaps most importantly, optimize your profits.

An Example of Customer Foresight in Practice

Codento has been working with some of Finland’s most ambitious companies to provide them with customer foresight capabilities. For example, Verkkokauppa.com, a leading online retailer, restructured its product categories based on the analysis of customer search patterns and purchase history.

It integrated several product management systems to streamline its operations and improve product availability. Additionally, it renewed its customer-facing front end by incorporating personalized product recommendations and a more intuitive user interface, all with the help of Codento’s customer foresight capabilities.

There Is Always Room for Creativity and Innovation

However, successful customer foresight isn’t solely reliant on data and technology; it’s equally about creativity and innovation. Companies must be agile and adaptable, willing to experiment with new ideas and concepts. Innovative solutions can surprise and delight customers, setting a business apart from its competitors.

The essence of customer foresight lies in the ability to adapt and evolve continuously. Consumer needs are dynamic and influenced by various factors such as cultural shifts, technological advancements, and global events. Therefore, businesses must remain agile and responsive to change to stay ahead in the market.

Customer Foresight is a Fundamental Strategy for Any Successful Business

In conclusion, customer foresight is a fundamental strategy for any successful business. By leveraging data, technology, consumer feedback, and innovative thinking, companies can better anticipate and fulfill customer needs. Understanding what customers want before they do and delivering it seamlessly is the hallmark of a customer-centric and forward-thinking business.

Watch our AI.cast to keep yourself up-to-date regading the recent AI developments.

About the author: Antti “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles at Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.

Unique AI-powered Employee Experience: Employee Help Desk with Google Cloud HR Agent Technology

Overview

Based on feedback from HR peers, we have created a unique AI solution that allows employees to easily find answers to HR-maintained guidelines, practices, and policies through a chat window embedded in the intranet. No more digging through files or web pages. This streamlines employee onboarding, saves time for staff, supervisors, and the HR department, and boosts employee satisfaction.

Challenges

Challenges facing organizations:

Increasing cost pressures and resource challenges for HR
Delayed productivity of new employees due to slow onboarding and difficulty finding information
Loss of tacit knowledge due to employee turnover
Increased workload for supervisors
Competition for skilled employees
Growth of information and challenges in finding the right information
Increased time pressures on employees
Remote work and reduced face-to-face interactions
Impact of employee motivation on performance
Less time and opportunity for employee training

Our Solution

Google Cloud and Codento offer a solution: AI-powered Employee Help Desk. This provides quick, accurate answers to employees seeking information on complex HR processes or documents (compensation, benefits, etc.) through:

Chatbot/Q&A and Search engine capabilities for HR documents without requiring engineering expertise, tuning, or configuration.
Agent Builder allows users to simply describe the configuration of the chat agent instead of defining it manually.

Implementation

Based on unique Google Cloud HR AI Agent technology and its turnkey lightweight implementation.

- A Generative AI HR Agent is an application that aims to achieve a goal by observing the world and acting upon it using the tools at its disposal
- User interface is the current HR intranet or equivalent HR portal, into which the HR agent’s chatbot is seamlessly integrated
- The agent has access to all necessary HR guidelines and documentation
- Learns over time to provide better and more relevant answers
- Adapts to updated materials
- Supports multiple languages

Codento, a Google Cloud Partner of the Year, configures and deploys the solution
- The client needs to create 25 test questions and 25 corresponding sample answers based on HR documentation. Codento handles the rest
- The solution is operational within few weeks of decision
- Can be implemented in the client’s existing Google Cloud environment, a new environment (additional setup cost), or Codento’s provided Google Cloud platform

Benefits

Speed: operational in just few weeks
Low cost: ask for an offer
Low risk: Codento has extensive experience with similar deployments using Google Cloud technology
Solution quality: Codento’s NPS is consistently over 70

Getting Your Company and Your Cloud AI-ready: Ebook to Rearchitect Your infrastructure to Unlock the Potential of AI

Getting your company and your cloud AI-ready: Ebook to rearchitect your infrastructure to unlock the potential of AI

Our partner Google Cloud created a guide for technical leaders like yourself with a roadmap to build a future-proof foundation for AI innovation. With an infrastructure that can fuel the next generation of your business, new opportunities to operationalize AI will empower teams to generate solutions to legacy challenges.

In this eBook, you will discover:

The infrastructure considerations that can determine AI success or failure — examining cost, scalability, security, and performance dimensions
Actionable strategies to evaluate AI platforms, optimize resources, and maximize the value of your AI tools
How and when to consider adopting managed machine learning offerings like Vertex AI and flexible container environments like Google Kubernetes Engine (GKE) to ease the operational burdens of your team
Best practices for leveraging specialized virtual machines (VMs) optimized for AI, including and equipped with GPUs and TPUs.

Ready to tap into the power of generative AI?

Submit your contact information to get the report:

The Executive’s Guide to Generative AI: Kickstart Your Generative AI Journey with a 10-Step Plan

Not sure where to start with generative AI?See what your industry peers are doing and use Google Cloud’s 10-step, 30-day plan to hit the ground running with your first use case

AI’s impact will be huge. Yet right now, only 15% of businesses and IT decision makers feel they have the expert knowledge needed in this fast-moving area.This comprehensive guide will not only bring you up to speed, but help you chart a clear path forward for adopting generative AI in your business. In it, you’ll find:

A quick primer on generative AI.
A 30-day step-by-step guide to getting started.
KPIs to measure generative AI’s impact.
Industry-specific use cases and customer stories from Deutsche Bank, TIME, and more.

Dive in today to discover how generative AI can help deliver new value in your business.

Submit your contact information to get the report:

Get Your Copy of Google Cloud 2024 Data and AI Trends Report

Your company is ready for generative AI. But is your data? In the AI-powered era, many organizations are scrambling to keep pace with the changes rippling across the entire data stack.

This new report from Google Cloud shares the findings from a recent survey of business and IT leaders about their goals and strategies for harnessing gen AI — and what it means for their data.

Get your copy to explore these five trends emerging from the survey:

Gen AI will speed the delivery of insights across organizations
The roles of data and AI will blur
Data governance weaknesses will be exposed
Operational data will unlock gen AI potential for enterprise apps
2024 will be the year of rapid data platform modernization

Submit your contact information below to get the report:

Google Cloud Next’24 Top 10 Highlights of the First Day

Authors: Codento Consulting Team

Google Cloud Momentum Continues

The Google Cloud Next event is taking place this week in Las Vegas showcases a strong momentum with AI and Google Cloud innovations with more than 30 000 participants.

Codento is actively participating to the event in Las Vegas with Ulf Sandlund and Markku Pulkkinen and remotely via the entire Codento team. Earlier on Tuesday Codento was awarded as the Google Cloud Service Partner of the Year in Finland.

As the battle is becoming more fierce among the hyperscalers we can fairly observe that Google Cloud has taken a great position going forward:

Rapid growth of Google Cloud with a $36 Billion run rate outpacing its hyperscaler peers on a percentage basis
Continuous deep investments in AI and Gen AI progress with over a million models trained
90% of unicorns use Google Cloud showcasing a strong position with startups
A lot of reference stories were shared. A broad range of various industries are now using Google Cloud and its AI stack
And strong ecosystem momentum globally in all geographies and locally

Top 10 Announcements for Google Cloud Customers

Codento consultants followed every second of the first day and picked our favorite top 10 announcements based on the value to Google Cloud customers:

1. Gemini 1.5 Pro available in public preview on Vertex AI. It can now process from 128,000 tokens up to 1 million tokens. Google truly emphasizes its multi-modal capabilities. The battle against other hyperscalers in AI is becoming more fierce.

2. Gemini is being embedded across a broad range of Google Cloud services addressing a variety of use cases and becoming a true differentiator, for example:

New BigQuery integrations with Gemini models in Vertex AI support multimodal analytics, vector embeddings, and fine-tuning of LLMs from within BigQuery, applied to your enterprise data.
Gemini in Looker enables business users to chat with their enterprise data and generate visualizations and reports

3. Gemini Code Assist is a direct competitor to GitHub’s Copilot Enterprise. Code Assist can also be fine-tuned based on a company’s internal code base which is essential to match Copilot.

4. Imagen 2. Google came out with the enhanced image-generating tool embedded in Vertex AI developer platform with more of a focus on enterprise. Imagen 2 is now generally available.

5. Vertex AI Agent Builder to help companies build AI agents. This makes it possible for customers to very easily and quickly build conversational agents and instruct and guide them the same way that you do humans. To improve the quality and correctness of answers from models, a process called grounding is used based on Google Search.

6. Gemini in Databases is a collection of AI-powered, developer-focused tools to create, monitor and migrate app databases.

7. Generative AI-powered security: number of new products and features aimed at large companies. These include Threat Intelligence, Chronicle to assist with cybersecurity investigations) and Security Command Center.

8. Hardware announcements: Nvidia’s next-generation Blackwell platform coming to Google Cloud in early 2025 and Google Cloud joins AWS and Azure in announcing its first custom-built Arm processor, dubbed Axion

9. Run AI anywhere, generative AI search packaged solution powered by Gemma designed to help customers easily retrieve and analyze data at the edge or on-premises with GDC, this solution will be available in preview in Q2 2024.

10. Data sovereignty. Google is renewing its focus on data sovereignty with emphasis on partnerships, less to building its own sovereign clouds.

There were also a lot of new announcements in the domains of employee productivity and Chrome, but we shall leave those areas for later discussion.

Conclusions

So far the list of announcements has been truly remarkable. As we anticipate the coming days of the Next event we are eager to get deeper into the details and understand what all this means in practice.

What is already known convinces us that Google Cloud and its AI approach continues to be completely enterprise-ready providing capabilities to support deployments from pilot to production.

To make all this real capable partners, like Codento, are needed to assist the entire journey: AI and data strategy, prioritized use cases, building the data foundation, implementing AI projects with strong grounding and integration, consider security and governance, and eventually build MLOps practices to scale the adoption.

For us partners, much anticipated news came in the form of a new specialization: Generative AI specialization will be available in June 2024. Codento is ready for this challenge with the practice and experience already in place.

To follow the Google Cloud Next 2024 event and announcements the best place is Google Cloud blog.

Celebrating Codento as the Google Cloud Partner of the Year in Finland

With the Award Comes a Shared Responsibility

Author: Anthony Gyursanszky, CEO, Codento

They say focus, determination, and hard work eventually result in a good outcome. So it has happened to us.

Team Codento, together with Team Google Cloud, embarked on a joint journey a few years back with the ambition to position Codento as a leading Google Cloud consulting company, identifying an emerging market and collaboration opportunity.

Through diligent efforts to achieve 2 Google Cloud specializations, 20 expertises, 35 professional certifications, and over 30 Google Cloud service deliveries with an NSAT rating of over 70, as well as recently expanding operations into Sweden, Codento was awarded the first-ever Google Cloud Partner of the Year award in Finland, presented in Las Vegas.

We are honored and thankful for this recognition from the Google Cloud teams, our customers, and our employees. This award underscores our commitment to delivering innovative AI, data, cloud, and application development consulting solutions and exemplary service to our Finnish and Swedish clients, showcasing the transformative power of Google Cloud.

As part of this rapidly growing Google Cloud partner ecosystem, we also understand that with the award comes responsibility.

We are committed to addressing the key topics in the Nordic IT and business landscape. There are four essential ambassadorial roles that Codento commits to taking an active role in from now on,

Sharing Information on the Continuously Evolving Capabilities of Google Cloud

In our unique role as Partner of the Year, we are well-positioned to share our insights into new Google Cloud products and features, their business value, differentiation versus other clouds, and our experiences on how to ramp up competencies and capabilities with Google Cloud rapidly.

We will continue to amplify the drumbeat of Google Cloud product news with business and technical analysis and interpretation in blogs, videos, events, and newsletters and serve as the primary point of contact in these matters.

Helping Nordic Organizations Become Leading Adopters of AI Innovations

According to various market forecasts, it is safe to say that Nordic organizations need to invest 3-10% of their revenue in AI development and capabilities in a few years to stay on par with their international peers.

These investments should not happen without an AI roadmap and persistent execution. Codento has taken a pioneering role in this by conducting over 100 free AI value workshops, aiming to identify high-value, low-complexity use cases that can be quickly adopted with a fast time to value.

So far, Codento teams have identified more than 300 different use cases and implemented many of them with customers, such as Hytest, whose AI adoption journey started with such a workshop.

With Codento’s extensive experience in AI use cases and ready-made offerings based on Google Cloud, we know how to deliver value rapidly with AI and are eager to share all these learnings in our events, videos, blogs, and newsletters, like AI.cast and AI newsletter.

Google is in a great position to bring continuous AI innovation to the market. The heart of AI innovation is Google Cloud’s innovative startup ecosystem. For example, more than 70% of Generative AI startups today have chosen to rely on Google Cloud capabilities. There is much to learn for the traditional organizations with the speed of innovation taking place there, and we, as Partner of the Year, are happy to share our learnings.

Solving the Critical Bottlenecks Customers Face in Their AI Scaling

While conducting our AI workshops, it has become clear that the most common bottleneck for AI scaling is the need for a data strategy and consistent implementation of it. With AI, it is paramount to ensure data quality and build proper means to collect, store, and update data.

In many cases, the lack of a general cloud strategy, architecture, and modern application portfolio also poses a challenge.

We advise organizations from start to finish and are committed to helping our Nordic customers overcome these hurdles as quickly as possible. Our novel data strategy offering is an excellent example of this.

Advising Customers Make Responsible and Proactive Cloud Decisions

As discussed with multiple international industry peers recently, organizations here in the Nordic region are more inclined to consolidate their cloud technology decisions on a single cloud of choice and become more dependent on that bet over time. This is different, for example, in the US, where organizations typically use several major cloud technologies.

While this single-cloud approach might have multiple benefits, such as easier competence management, the recent AI disruption provides a unique opportunity to consider complementary alternatives.

We see that continuing with the current cloud, replacing it, or complementing it with other cloud alternatives is always a critical business decision and should be regularly assessed with a fresh mind. In the Nordics, this seems to be a reactive rather than a proactive process.

The benefits of a multi-cloud approach are broad:

Cost optimization
More flexible cloud resource usage
Access to broader and more targeted innovations
Better vendor-lock-in management
Sustainability optimization

As a Partner of the Year, we are extremely enthusiastic about this area and will be evangelizing these themes and benefits heavily in the coming months with our Nextgen Foundation offering and a fresh view of an AI-optimized cloud strategy.

Looking Ahead

It is an honor for our whole Codento team to be the Partner of the Year in this growing Google Cloud ecosystem. We are excited and committed to being a prime example of an active and professional ambassador of Google Cloud and consultancy power in the years to come.

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with over 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. He has also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

Your Software Is About to Get a Lot Smarter

Author: Antti Pohjolainen, Codento

Software Intelligence

The age of software intelligence has arrived, fundamentally reshaping the way software is built and deployed. Artificial intelligence (AI) is no longer just an exciting buzzword; it’s transforming the very heart of software creation. Let’s delve into three key viewpoints highlighting AI’s disruptive potential in the software development landscape.

1.Build AI-driven Software Strategy

Imagine software that can learn, adapt, and even make decisions. This isn’t science fiction – it’s the future fueled by AI. Companies embracing this transformation must craft AI-driven software strategies, prioritizing:

Intelligent features: Embed AI algorithms to power predictive analytics, process automation, natural language understanding, computer vision, and more. Users no longer just interact with software as it anticipates and guides them.
Data-centric design: AI thrives on data. Architect systems from the ground up to gather, process, and leverage massive datasets for insights previously unimaginable.
Ethical considerations: Alongside technical aspects, address bias, transparency, and the responsible use of intelligent software.

2.Supercharge Your Software Development with AI

AI is becoming an indispensable tool in the software developer’s arsenal. Consider how it can streamline and enhance your workflow:

Code generation and optimization: AI helps write more efficient code, suggest better algorithms, and identify potential errors early in the process.
Intelligent testing: AI-powered testing automates routine cases, detects subtle bugs, and generates scenarios humans might overlook.
Personalized user experiences: AI tailors interfaces, suggests features, and provides proactive support, leading to unprecedented levels of user satisfaction.

3.Complement Your Development Capacity by Leveraging Codento’s Experienced AI Experts

Not every company has in-house AI expertise, and navigating the complex landscape of AI tools and platforms can be daunting. Codento bridges this gap with a team of seasoned AI specialists dedicated to accelerating your software’s intelligence:

Custom-tailored AI solutions: We partner with you to understand your unique business needs and develop AI solutions that solve real-world problems.
Strategic guidance: Benefit from our insights on how AI can revolutionize your software. We help shape a future-proof roadmap.
Seamless integration: Our deep understanding of software development ensures that AI components are effortlessly embedded within your existing systems and processes.

The Path Forward

Software intelligence is more than a trend; it’s an inevitable evolution demanding focused attention. Companies that embrace it will gain a significant competitive edge, delivering smarter, more efficient, and truly groundbreaking software experiences. Join this revolution and let Codento be your experienced guide in this exciting AI-driven journey.

References

Choicely Enhanced No-code App Builder with the Google Cloud Generative AI Capabilities

Fastems Adding AI-accelerated Smart Scheduling Capabilities into an Industrial SaaS Offering

Agileday Scaling Their SaaS Business on a Rock-Solid Google Cloud Foundation

Harnessing AI Power: Building the Next Generation Foundation

Author: Antti Pohjolainen, Codento

Artificial Intelligence (AI), that field which imbues machines with the power to ‘think’, is no longer solely the domain of science fiction. AI and its associated technologies are revolutionizing the way businesses operate, interact with customers, and ultimately shape the future. AI will have to sit at the core if organizations wish to be truly future-proof and embrace sustainable growth.

Yet, building the infrastructure to handle AI-driven projects can be a significant challenge for those organizations not born ‘digital natives’. Here we’ll outline some strategic pathways towards an integrated AI future that scales your business success.

Beyond Hype: Real-World Benefits of an AI Foundation

AI sceptics abound, perhaps wary of outlandish promises and Silicon Valley hyperbole. Let’s cut through the noise and look at some solid reasons to build a future upon a NextGen AI Foundation:

Efficiency reimagined: Automation remains a prime benefit of AI systems. Think about repetitive manual tasks – they can often be handled more quickly and accurately by intelligent algorithms. That frees up your precious human resources to focus on strategic initiatives and complex problem-solving that truly drive the business forward.
Data-driven decisions: We all have masses of data – often, organizations literally don’t know what to do with it all. AI is the key to transforming data into actionable insights. Make faster, better-informed choices from product development to resource allocation.
Predictive powers: Anticipate customer needs, optimize inventory, forecast sales trends – AI gives businesses a valuable window into the future and the chance to act with precision. It mitigates risks and maximizes opportunities.

Take our customers BHG as an example. They needed to implement a solid BI platform to service the whole company now and in the future. With the help of Codento’s data experts, BHG now has a highly automated, robust financial platform in production. Read more here.

Constructing Your AI Foundation: Key Considerations

Ready to join the AI-empowered leagues? It’s critical to start with strong groundwork:

Cloud is King: Cloud-based platforms provide the flexibility, scalability, and computing power that ambitious AI projects demand. Look for platforms with specialized AI services to streamline development and reduce overhead.
Data is The Fuel: Your AI systems are only as good as the data they’re trained on. Make sure you have robust data collection, cleansing, and governance measures in place. Remember, high-quality data yields greater algorithmic accuracy.
The Human Touch: Don’t let AI fears take hold. This isn’t about replacing humans but supplementing them. Re-skill, re-align, and redeploy your teams to work with AI tools. AI’s success relies on collaboration, and ethical AI development should be your mantra.
Start Small, Aim Big: Begin with focused proof-of-concept projects to demonstrate value before expanding your AI commitment. A well-orchestrated, incremental approach can help manage complexity and gain acceptance throughout your organization.

The Road Ahead: AI’s Power to Transform

It’s undeniable that building a Next Generation Foundation with AI requires effort and careful planning. But, the potential for businesses of all sizes is breathtaking. Imagine streamlined operations, enhanced customer experiences, and insights that lead to unprecedented successes.

AI isn’t just the future – it’s the foundation for the businesses that will be thriving in the future. The time to join the AI revolution is now. The rewards are simply too great to be left on the table.

About the author: Antti “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.

Smart Operations: Embracing AI for Efficiency and Growth

Author: Antti Pohjolainen, Codento

As mentioned in the previous blog post, AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers. This also holds true for the domain of operations, where AI is poised to revolutionize traditional processes, driving efficiency, enhancing productivity, and paving the way for sustainable growth.

Unlocking the Potential of AI for Operations

AI’s impact on operations extends across various facets of business, including:

Predictive Maintenance: AI algorithms can analyze vast amounts of data, including sensor readings and historical performance records, to predict equipment failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and enhances overall asset utilization.
Smart Scheduling: AI-powered scheduling solutions can optimize resource allocation and task assignment, ensuring that employees are matched with the right tasks at the right time. This leads to improved productivity, reduced overtime costs, and improved employee satisfaction.
Supply Chain Optimization: AI can analyze demand patterns, identify disruptions, and optimize inventory levels, resulting in a more efficient and responsive supply chain. This translates into reduced costs, improved delivery times, and enhanced customer satisfaction.
Risk Mitigation: AI can monitor operational data and identify anomalies or patterns that could indicate potential risks. This allows businesses to take preemptive action, avert costly incidents, and protect their assets and reputation.

Codento has been working together with some of the Finnish forefront companies in manufacturing to implement AI in their operations. Take Fastems for example where Codento implemented AI-powered Smart Scheduling and predictive maintenance capabilities. For more information, please see our reference case stories here and here.

The Journey Towards Smart Operations

Implementing AI in operations requires a strategic approach that considers the specific needs and challenges of each organization. Key steps include:

Identifying Pain Points: The first step is to identify areas where AI can bring the most significant benefits, such as reducing costs, improving efficiency, or enhancing decision-making.
Data Preparation: High-quality data is essential for AI to function effectively. This involves cleaning, organizing, and standardizing data to ensure its accuracy and reliability.
Model Development and Deployment: AI models are developed using machine learning algorithms that train on the prepared data. These models are then deployed to production environments to automate tasks and provide insights.
Continuous Monitoring and Improvement: AI models are not static; they need to be continuously monitored and updated as data and business conditions evolve. This ensures that they remain accurate, relevant, and effective.

About the author: Antti “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.

What Does a CEO Do?

What Does the CEO of an AI-driven Software Consulting Firm Actually Do During a Workday?

Author: Anthony Gyursanszky, CEO, Codento

This is a question that comes up from time to time. When you have a competent team around you, the answer is simple: I consult myself, meet existing clients, or sell our consulting services to new clients. Looking back at the past year, my own statistics indicate that my personal consulting has been somewhat limited this time, and more time has been spent with new clients.

And How about My Calendar?

My calendar shows, among other things, 130 one-on-one discussions with clients, especially focusing on the utilization of artificial intelligence across various industries and with leaders and experts from diverse backgrounds. Out of these, 40 discussions led to scheduling in-depth AI workshops on our calendars. I’ve already conducted 25 of these workshops with our consultants, and almost every client has requested concrete proposals from us for implementing the most useful use cases. Several highly intriguing actual implementation projects have already been initiated.

The numbers from my colleagues seem quite similar, and collectively, through these workshops, we have identified nearly 300 high-value AI use cases with our clients. This indicates that there will likely be a lot of hustle in the upcoming year as well.

What Are My Observations?

In leveraging artificial intelligence, there’s a clear shift in the Nordics from hesitation and cautious contemplation to actual business-oriented plans and actions. Previously, AI solutions developed almost exclusively for product development have now been accompanied by customer-specific implementations sought by business functions, aiming for significant competitive advantages in specific business areas.

My Favorite Questions

What about the next year? My favorite questions:

Have you analyzed the right areas to invest in for leveraging AI in terms of your competitiveness?
If your AI strategy = ChatGPT, what kind of analysis is it based on?
Assuming that the development of AI technologies will accelerate further and the options will increase, is now the right time to make a strict technology/supplier choice?
If your business data isn’t yet ready for leveraging AI, how long should you still allow your competitors to have an edge?

What would be your own answers?

About the author:

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Codento Levels Up Serverless Expertise at Google Cloud Nordics Serverless Summit 2023

Authors: Olli-Pekka Lamminen, Google Bard

In November, Codento was thrilled to be invited to attend the Google Cloud Nordics Serverless Summit 2023 in Sunnyvale, California. This two-day event, held at the Google Cloud campus, was packed with exciting updates, in-depth discussions, and valuable networking opportunities.

Cloud-Powered Efficiency: Cost, Performance, and Creativity

The ability to drive down operational costs featured heavily at the Serverless Summit. With a pay-as-you-go pricing model and reduced price for idle instances Cloud Run is one of the most cost effective ways for businesses to run their workloads in a serverless environment. Flexible scaling from zero aligns perfectly with the dynamic nature of serverless applications, ensuring that organisations only pay for the resources they consume. This together with low management overhead and ease of development makes serverless technology accessible and affordable for businesses of all sizes.

Synthetic monitoring with Cloud Ops provides proactive insights into application performance and health, enabling businesses to identify and address potential issues before they impact real users. By simulating user interactions, this monitoring tool proactively identifies and alerts about potential problems, allowing businesses to maintain scalable and responsiveoperations. Together with capabilities like Log Analytics and AIOps, the Cloud Operations suite empowers businesses to prevent and address performance issues proactively, ensuring a consistently positive user experience.

Cloud based development environments, enhanced with Duet AI, bring the power of artificial intelligence to the creative workspace. Duet AI acts as an intelligent assistant, providing real-time feedback and suggestions, enabling creative professionals to enhance their productivity and achieve their visions. Google’s commitment to protecting its customers using generative AI products, like Duet AI and Vertex AI, in the event of copyright infringement lawsuits further reinforces the company’s dedication to innovation and responsible AI development.

Google’s Focus on Developer Experience with Cloud Run

It was evident that Google is placing a strong emphasis enhancing developer experience, focusing on making Cloud Run even more developer-friendly and efficient. The company discussed several new features and enhancements designed to streamline the process of building and deploying serverless applications, all of which are already available at least in preview today. These include:

Accelerated Build and Deployment: Google is streamlining the build and deployment process for Cloud Run applications with optimised buildpacks, making it easier and faster for developers to get their applications up and running quickly, efficiently and securely.

Improved Performance and Scalability: Google is continuously improving the performance and scalability of Cloud Run, ensuring that applications can handle even the most demanding workloads. Cloud Run has demonstrated the ability to scale from zero to thousands within mere seconds.

Ease of Integration with Other Google Cloud Offerings: With Cloud Run integrations, developers can easily take other Google Cloud services, such as Cloud Load Balancing, Firebase Hosting and Cloud Memorystore, in use with their serverless applications. Products like Eventarc allow developers to establish seamless communication between serverless applications and other cloud services, facilitating event-driven workflows and real-time data processing.

Simplified Networking and Security: While Cloud Run integrations make using load balancers a breeze, Direct VPC egress enables serverless applications to directly access resources within a VPC, eliminating the need for a proxy. This direct communication enhances performance and minimises latency. IAP provides a secure gateway for external users to access serverless applications, leveraging Google’s authentication infrastructure to verify user identities before granting access.
Effortless Workload Migration: Cloud Run and GKE Autopilot can run the same container images without any modifications, and their resource descriptions are nearly identical. This makes it incredibly easy to move your workloads between the two platforms, depending on your specific needs or as those needs evolve.

Project Starline and the Future of Internet in Space

Beyond the technical discussions, we also had the opportunity to explore Project Starline, Google’s experimental 3D video communication technology. Project Starline uses a combination of hardware and software to create a more natural and immersive video conferencing experience.

We also had the pleasure of discussing the future of the internet in space with Vint Cerf, a pioneer in the field of computer networking and often referred to as the “father of the Internet.” Cerf shared his insights on the challenges and opportunities of building a reliable and accessible internet infrastructure in space.

An Invaluable Experience that Spurs Innovation

Overall, the Google Cloud Nordics Serverless Summit 2023 proved to be an invaluable experience for us. We gained insights into the latest advancements in serverless technology, learned from Google experts, and connected with other industry leaders. We are excited to apply our newfound knowledge to help our customers build and deploy even more innovative serverless applications.

About the Authors

Olli-Pekka Lamminen is an experienced software and cloud architect at Codento, with over 20 years of experience in the IT industry. Olli-Pekka is utilising his extensive background and knowledge to design and implement robust, scalable software solutions for our customers. His deep understanding of cloud technologies and telecommunications empowers him to deliver exceptional solutions that meet the evolving needs of businesses.

Google Bard is a powerful language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. It is still under development, but we are excited about its potential to help people in a variety of ways.

Learn more about Codento’s software intelligence services:

Top 4 Picks by Codento Team – fooConf, Helsinki

Authors: Codento consultants Samuel Mäkelä, Iiro Niemi, Olli Alm & Timo Koola

On Tuesday November 7th the second installment of fooConf was held at Hakaniemi, Helsinki. We (eight of us!) spent the day in the conference and asked our team what their one pick of the day was.

Here are our top 4 of the fooConf Helsinki 2023!

#1 Adam Tornhill: The business impact of code quality (top pick by Samuel)

To me, Adam Tornhill’s conference talk was quite mind-blowing. His ”10 years of trauma & research in technical debt” not only translated complex research data into clear visualizations about technical debt and code complexity, but also underscored the significant business impact of tackling these challenges. Through his presentation, Tornhill illuminated how addressing technical debt can lead to improved code quality, reduced maintenance costs and ultimately contribute to the overall success of a software project. It was a fascinating blend of in-depth research and practical insights, leaving a lasting impression on how we perceive and approach software development from both technical and business perspectives.

Book: The code as a crime scene (2nd. ed, 2023)
White paper: https://codescene.com/hubfs/web_docs/Business-impact-of-low-code-quality.pdf
Research publication: https://arxiv.org/abs/2203.04374
Forbes: https://www.forbes.com/sites/forbestechcouncil/2023/09/07/the-code-quality-advantage-how-empirical-data-shatters-the-speed-vs-quality-myth/

#2 Mete Atamel: WebAssembly beyond the browser (by Iiro)

Mete Atamel from Google discussed the evolving use of WebAssembly technology outside the browser environment. He emphasized that WebAssembly on the server, particularly with the WebAssembly System Interface (WASI), offers a compelling alternative to traditional methods of running applications, such as through virtual machines or containers. This perspective aligns with findings from the CNCF 2022 Annual Survey, which indicates a growing consensus that “Containers are the new normal and Wasm as the future”. Leveraging Wasm with WASI offers several notable benefits over containers, such as faster execution, reduced footprint, enhanced security and portability. However, despite this enthusiasm, it’s important to recognize that we are still some distance from having fully-featured and stable WebAssembly projects for server-side applications. This gap highlights the ongoing development and the need for further innovation in the field.

#3 Guillaume LaForge: Generative AI in practice: Concrete LLM use cases in Java, with the PaL

M API (by Olli)

Guillaume presented hands-on examples on how to utilize large language models via Google PaLM API. PaLM (Pathways Language Model) is a single, generalized language model that can be adjusted to specific domains or sizes (PaLM2). In his presentation, Guillaume utilized Google PaLM APIs and Langchain for building a bedtime story generator in Groovy.

Links below:

Slideset: https://speakerdeck.com/glaforge/generative-ai-in-practice-concrete-llm-use-cases-in-java-with-the-palm-api

Bed time stories for kids: https://bed-time-stories.web.app/
Pathway language model: https://blog.research.google/2022/04/pathways-language-model-palm-scaling-to.html
Palm2:https://blog.google/technology/ai/google-palm-2-ai-large-language-model/
Langchain4J https://github.com/langchain4j/langchain4j
Langchain home https://python.langchain.com/docs/get_started/introduction
Palm APIs https://developers.generativeai.google/products/palm

#4 Marit van Dijk: Reading Code (by Timo)

Presentation by Marit van Dijk (link to slides) starts with a simple observation: “We spend a lot of time learning to write code, while spending little to no time learning to read code. Meanwhile, we often spend more time reading code than actually writing it. Shouldn’t we be spending at least the same amount of time and effort improving this skill?“.

These questions take us into fascinating topics ranging from how to help our brain understand other programmers and our shared code (see book Programmer’s Brain by Felienne Hermans) to structured practices that build up our code reading capabilities. The practice called “Code Reading Club” is one way to practice code reading systematically in small groups. This presentation made me want to try this with team Codento. Stay tuned, we will tell you how it went!

Introduction to AI in Business Blog Series: Unveiling the Future

Author: Antti Pohjolainen, Codento

Foreword

In today’s dynamic business landscape, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping the way industries operate and paving the way for innovation. Companies of all sizes are implementing AI-based solutions.

AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers.

In discussions and workshops with our customers, we have identified close to 250 different use cases for a wide range of industries.

Our AI in Business Blog Series

In addition to publishing our AI.cast on-demand video production, we summarize our key learnings and insights in the “AI in Business” blog series.

This blog series will delve into the multifaceted role AI plays in reshaping business operations, customer relations, and overall software intelligence. In the following blog posts, each post has a specific viewpoint concentrating on a business need. Each perspective contains examples and customer references of innovative ways to implement AI.

In the next part – Customer Foresight – we’ll discuss how AI will provide businesses with better customer understanding based on their buying behavior, better use of various customer data, and analyzing customer feedback.

In part three – Smart Operations – we’ll look at examples of benefits customers have gained by implementing AI into their operations, including smart scheduling and supply chain optimization.

In part four – Software Intelligence – we’ll concentrate on using AI in software development.

Implementing AI to solve your business needs could provide better decision-making capabilities, increase operational efficiency, improve customer experiences, and help mitigate risks.

The potential of AI in business is vast, and these blog posts aim to illuminate the path toward leveraging AI for enhanced business growth, efficiency, and customer satisfaction. Join us in unlocking the true potential of AI in the business world.

Stay tuned for our next installment: “Customer Foresight” – Unveiling the Power of Predictive Analytics in Understanding Customer Behavior.!

About the author: Antti “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.

Google Cloud Nordic Summit 2023: Three Essential Technical Takeaways

Authors, Jari Timonen, Janne Flinck, Google Bard

Codento participated with a team of six members in the Google Cloud Nordic Summit on 19-20 September 2023, where we had the opportunity to learn about the latest trends and developments in cloud computing.

In this blog post, we will share some of the key technical takeaways from the conference, from a developer’s perspective.

Enterprise-class Generative AI for Large Scale Implementtation

One of the most exciting topics at the conference was Generative AI (GenAI). GenAI is a type of artificial intelligence that can create new content, such as text, code, images, and music. GenAI is still in its early stages of development, but it has the potential to revolutionize many industries.

At the conference, Google Cloud announced that its GenAI toolset is ready for larger scale implementations. This is a significant milestone, as it means that GenAI is no longer just a research project, but a technology that

can be used to solve real-world problems.

One of the key differentiators of Google Cloud’s GenAI technologies is their focus on scalability and reliability. Google Cloud has a long track record of running large-scale AI workloads, and it is bringing this expertise to the GenAI space. This makes Google Cloud a good choice for companies that are looking to implement GenAI at scale.

Cloud Run Helps Developers to Focus on Writing Code

Another topic that was covered extensively at the conference was Cloud Run. Cloud Run is a serverless computing platform that allows developers to run their code without having to manage servers or infrastructure. Cloud Run is a simple and cost-effective way to deploy and manage web applications, microservices, and event-driven workloads.

One of the key benefits of Cloud Run is that it is easy to use. Developers can deploy their code to Cloud Run with a single command, and Google Cloud will manage the rest. This frees up developers to focus on writing code, rather than managing infrastructure.

Google just released Direct VPC egress functionality to Cloud Run. It lowers the latency and increases throughput for connections to your VPC network. It is more cost effective than serverless VPC connectors which used to be the only way to connect your VPC to Cloud Run.

Another benefit of Cloud Run is that it is cost-effective. Developers only pay for the resources that their code consumes, and there are no upfront costs or long-term commitments. This makes Cloud Run a good choice for all companies.

Site Reliability Engineering (SRE) Increases Customer Satisfaction

Site Reliability Engineering (SRE) is a discipline that combines software engineering and systems engineering to ensure the reliability and performance of software systems. SRE is becoming increasingly important as companies rely more and more on cloud-based applications.

At the conference, Google Cloud emphasized the importance of SRE for current and future software teams and companies.

One of the key benefits of SRE is that it can help companies improve the reliability and performance of their software systems. This can lead to reduced downtime, improved customer satisfaction, and increased revenue.

Another benefit of SRE is that it can help companies reduce the cost of operating their software systems. SRE teams can help companies identify and eliminate waste, and they can also help companies optimize their infrastructure.

Conclusions

The Google Cloud Nordic Summit was a great opportunity to learn about the latest trends and developments in cloud computing. We were particularly impressed with Google Cloud’s GenAI toolset and Cloud

Run platform. We believe that these technologies have the potential to revolutionize the way that software is developed and deployed.

We were also super happy

that Codento was awarded with the Partner Impact 2023 Recognition in Finland by Google Cloud Nordic team. Codento received praise for deep expertise in Google Cloud services and market impact, impressive NPS score, and achievement of the second Google Cloud specialization.

About the Authors

Jari Timonen, is an experienced software professional with more than 20 years of experience in the IT field. Jari’s passion is to build bridges between the business and the technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.

Janne Flinck is an AI & Data Lead at Codento. Janne joined Codento from Accenture 2022 with extensive experience in Google Cloud Platform, Data Science, and Data Engineering. His interests are in creating and architecting data-intensive applications and tooling. Janne has three professional certifications and one associate certification in Google Cloud and a Master’s Degree in Economics.

Bard is a conversational generative artificial intelligence chatbot developed by Google, based initially on the LaMDA family of large language models (LLMs) and later the PaLM LLM. It was developed as a direct response to the rise of OpenAI’s ChatGPT, and was released in a limited capacity in March 2023 to lukewarm responses, before expanding to other countries in May.

100 Customer Conversations Shaped Our New AI and Apps Service Offering

Author: Anthony Gyursanszky, CEO, Codento

Foreword

A few months back, in a manufacturing industry event: Codento just finished our keynote together with Google and our people started mingling among the audience. Our target was to agree on a follow-up discussions about how to utilize Artificial Intelligence (AI) and modern applications for their business.

The outcome of that mingling session was staggering. 50% of the people we talked with wanted to continue the dialogue with us after the event. The hit rate was not 10%, not 15%, but 50%.

We knew before already that AI will change everything, but with this, our confidence climbed to another level . Not because we believed in this, but because we realized that so many others did, too.

AI will change the way we serve customers and manufacture things, the way we diagnose and treat illnesses, the way we travel and commute, and the way we learn. AI is everywhere, and not surprisingly, it is also the most common topic that gets executives excited and interested in talking.

AI does not solve the use cases without application innovations. Applications integrate the algorithms to an existing operating environment, they provide required user interfaces, and they handle the orchestration in a more complex setup.

We address your industry- and role-specific needs with AI and application innovations

We at Codento have been working with AI and Apps for several years now. Some years back, we also sharpened our strategy to be the partner of choice in Finland for Google Cloud Platform-based solutions in the AI and applications innovation space.

During the past six months, we have been on a mission to workshop with as many organizations as possible about their needs and aspirations for AI and Apps. This mission has led us to more than a hundred discussions with dozens and dozens of people from the manufacturing industry to retail and healthcare to public services.

Based on these dialogues, we concluded that it is time for Codento to move from generic technology talks to more specific messages that speak the language of our customers.

Thus, we are thrilled to introduce our new service portfolio, shaped by those extensive conversations with various organizations’ business, operations, development, and technology experts.

Tailored precisely to address your industry and role-specific requirements, we now promise you more transparent customer foresight, smarter operations, and increased software intelligence – all built on a future-proof, next-generation foundation on Google Cloud.

These four solution areas will form the pillars of Codento’s future business. Here we go.

AI and Apps for Customer Foresight

As we engaged with sales, marketing and customer services officers we learned that the majority is stuck with limited visibility of customer understanding and of the impact their decisions and actions have on their bottom line. AI and Apps can change all this.

For example, with almost three out of four online shoppers expecting brands to understand their unique needs, the time of flying blind on marketing, sales, and customer service is over.

Codento’s Customer Foresight offering is your key to thriving in tomorrow’s markets.

Use data and Google’s innovative tech, trained on the world’s most enormous public datasets, to find the right opportunities, spot customers’ needs, discover new markets, and boost sales with more intelligent marketing.
Exceed your customers’ expectations by elevating your retention game with great experiences based on new technology. Keep customers returning by foreseeing their desires and giving them what they want when and how they want it – even before they realize their needs themselves.
Optimize Your Profits with precise data-driven decisions based on discovering your customers’ value with Google’s ready templates for calculating Customer Lifetime Value. With that, you can focus on the best customers, make products that sell, and set prices that work.

AI and Apps for Smart Operations

BCG has stated that 89% of industrial companies plan to implement AI in their production networks. As we have been discussing with the operations, logistics and supply chain directors, we have seen this to be true – the appetite is there.

Our renewed Smart Operations offering is your path to operational excellence and increased resilience. You should not leave this potential untapped in your organization.

By smart scheduling your operations, we will help streamline your factory, logistics, projects, and supply chain operations. With the help of Google’s extensive AI tools for manufacturing and logistics operations, you can deliver on time, within budget, and with superior efficiency.
Minimize risks related to disruptions, protect your reputation, and save resources, thereby boosting employee and customer satisfaction while cutting costs.
Stay one step ahead with the power of AI, transparent data, and analytics. Smart Operations keeps you in the know, enabling you to foresee and tackle disruptions before they even happen.

AI and Apps for Software Intelligence

For the product development executives of software companies, Codento offers tools and resources for unleashing innovation. The time to start benefiting from AI in software development is now.

Gartner predicts that 15% of new applications will be automatically generated by AI in the year 2027 – that is, without any interaction with a human. As a whopping 70% of the world’s generative AI startups already rely on Google Cloud’s AI capabilities, we want to help your development organization do the same.

Codento’s support for building an AI-driven software strategy will help you confidently chart your journey. You can rely on Google’s strong product vision and our expertise in harnessing the platform’s AI potential.
Supercharge your software development and accelerate your market entry with cutting-edge AI-powered development tools. With Codento’s experts, your teams can embrace state-of-the-art DevOps capabilities and Google’s cloud-native application architecture.
When your resources fall short, you can scale efficiently by complementing your development capacity with our AI and app experts. Whether it’s Minimum Viable Products, rapid scaling, or continuous operations, we’ve got your back.

Nextgen Foundation to enable AI and Apps

While the business teams are moving ahead with AI and App initiatives related to Customer Foresight, Smart Operations, and Software Intelligence IT functions are often bound to legacy IT and data architectures and application portfolios. This creates pressure for the IT departments to keep up with the pace.

All the above-mentioned comes down to having the proper foundation to build on, i.e., preparing your business for the innovations that AI and application technologies can bring. Moving to a modern cloud platform will allow you to harness the potential of AI and modern applications, but it is also a cost-cutting endeavor.BCG has studied companies that are forerunners in digital and concluded that they can save up to 30% on their IT costs when moving applications and infrastructure to the cloud.

Future-proof your architecture and operations with Google’s secure, compliant, and cost-efficient cloud platform that will scale to whatever comes next. Whether you choose a single cloud strategy or embrace multi-cloud environments, Codento has got you covered.
You can unleash the power and amplify the value of your data through real-time availability, sustainable management, and AI readiness. With Machine Learning Ops (MLOps), we streamline your organization’s scaling of AI usage.
We can also help modernize your dated application portfolio with cloud-native applications designed for scale, elasticity, resiliency, and flexibility.

Sharpened messages wing Codento’s entry to the Nordic market

With these four solution areas, we aim to discover the solutions to your business challenges quickly and efficiently. We break the barriers between business and technology with our offerings that speak the language of the target person. We are dedicated to consistently delivering solutions that meet your needs and learn and become even more efficient over time.

Simultaneously, we eagerly plan to launch Codento’s services and solutions to the Nordic market. Our goal is to guarantee that our customers across the Nordics can seize the endless benefits of Google’s cutting-edge AI and application technologies without missing a beat.

About the author:

AI in Manufacturing: AI Visual Quality Control

Author: Janne Flinck

Introduction

Inspired by the Smart Industry event, we decided to start a series of blog posts that tackle some of the issues in manufacturing with AI. In this first section, we will talk about automating quality control with vision AI.

Manufacturing companies, as well as companies in other industries like logistics, prioritize the effectiveness and efficiency of their quality control processes. In recent years, computer vision-based automation has emerged as a highly efficient solution for reducing quality costs and defect rates.

The American Society of Quality estimates that most manufacturers spend the equivalent of 15% to 20% of revenues on “true quality-related costs.” Some organizations go as high as 40% cost-of-quality in their operations. Cost centers that affect quality in manufacturing come in three different areas:

Appraisal costs: Verification of material and processes, quality audits of the entire system, supplier ratings
Internal failure costs: Waste of resources or errors from poor planning or organization, correction of errors on finished products, failure of analysis regarding internal procedures
External failure costs: Repairs and servicing of delivered products, warranty claims, complaints, returns

Artificial intelligence is helping manufacturers improve in all these areas, which is why leading enterprises have been embracing it. According to a 2021 survey of more than 1,000 manufacturing executives across seven countries interviewed by Google Cloud, 39% of manufacturers are using AI for quality inspection, while 35% are using it for quality checks on the production line itself.

Top 5 areas where AI is currently deployed in day-to-day operations:

Quality inspection 39%
Supply chain management 36%
Risk management 36%
Product and/or production line quality checks 35%
Inventory management 34%

Source: Google Cloud Manufacturing Report

With the assistance of vision AI, production line workers are able to reduce the amount of time spent on repetitive product inspections, allowing them to shift their attention towards more intricate tasks, such as conducting root cause analysis.

Modern computer vision models and frameworks offer versatility and cost-effectiveness, with specialized cloud-native services for model training and edge deployment further reducing implementation complexities.

Solution overview

In this blog post, we focus on the challenge of defect detection on assembly and sorting lines. The real-time visual quality control solution, implemented using Google Clouds Vertex AI and AutoML services, can track multiple objects and evaluate the probability of defects or damages.

The first stage involves preparing the video stream by splitting the stream into frames for analysis. The next stage utilizes a model to identify bounding boxes around objects.

Once the object is identified, the defect detection system processes the frame by cutting out the object using the bounding box, resizing it, and sending it to a defect detection model for classification. The output is a frame where the object is detected with bounding boxes and classified as either a defect or not a defect. The quick processing time enables real-time monitoring using the model’s output, automating the defect detection process and enhancing overall efficiency.

The core solution architecture on Google Cloud is as follows:

Implementation details

In this section I will touch upon some of the parts of the system, mainly what it takes to get started and what things to consider. The dataset is self created from objects I found at home, but this very same approach and algorithm can be used on any objects as long as the video quality is good.

Here is an example frame from the video, where we can see one defective object and three non-defective objects:

We can also see that one of the objects is leaving the frame on the right side and another one is entering the frame from the left.

The video can be found here.

Datasets and models overview

In our experiment, we used a video that simulates a conveyor belt scenario. The video showed objects moving from the left side of the screen to the right, some of which were defective or damaged. Our training dataset consists of approximately 20 different objects, with four of them being defective.

For visual quality control, we need to utilize an object detection model and an image classification model. There are three options to build the object detection model:

Train a model powered by Google Vertex AI AutoML
Use the prebuilt Google Cloud Vision API
Train a custom model

For this prototype we decided to opt for both options 1 and 2. To train a Vertex AI AutoML model, we need an annotated dataset with bounding box coordinates. Due to the relatively small size of our dataset, we chose to use Google Clouds data annotation tool. However, for larger datasets, we recommend using Vertex AI data labeling jobs.

For this task, we manually drew bounding boxes for each object in the frames and annotated the objects. In total, we used 50 frames for training our object detection model, which is a very modest amount.

Machine learning models usually require a larger number of samples for training. However, for the purpose of this blog post, the quantity of samples was sufficient to evaluate the suitability of the cloud service for defect detection. In general, the more labeled data you can bring to the training process, the better your model will be. Another obvious critical requirement for the dataset is to have representative examples of both defects and regular instances.

The subsequent stages in creating the AutoML object detection and AutoML defect detection datasets involved partitioning the data into training, validation, and test subsets. By default, Vertex AI automatically distributes 80% of the images for training, 10% for validation, and 10% for testing. We used manual splitting to avoid data leakage. Specifically, we avoid having sets of sequential frames.

The process for creating the AutoML dataset and model is as follows:

As for using the out-of-the-box Google Cloud Vision API for object detection, there is no dataset annotation requirement. One just uses the client libraries to call the API and process the response, which consists of normalized bounding boxes and object names. From these object names we then filter for the ones that we are looking for. The process for Vision API is as follows:

Why would one train a custom model if using Google Cloud Vision API is this simple? For starters, the Vision API will detect generic objects, so if there is something very specific, it might not be in the labels list. Unfortunately, it looks like the complete list of labels detected by Google Cloud Vision API is not publicly available. One should try the Google Cloud Vision API and see if it is able to detect the objects of interest.

According to Vertex AI’s documentation, AutoML models perform optimally when the label with the lowest number of examples has at least 10% of the examples as the label with the highest number of examples. In a production case, it is important to capture roughly similar amounts of training examples for each category.

Even if you have an abundance of data for one label, it is best to have an equal distribution for each label. As our primary aim was to construct a prototype using a limited dataset, rather than enhancing model accuracy, we did not tackle the problem of imbalanced classes.

Object tracking

We developed an object tracking algorithm, based on the OpenCV library, to address the specific challenges of our video scenario. The specific trackers we tested were CSRT, KCF and MOSSE. The following rules of thumb apply in our scenario as well:

Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput
Use KCF when you need faster FPS throughput but can handle slightly lower object tracking accuracy
Use MOSSE when you need pure speed

For object tracking we need to take into account the following characteristics of the video:

Each frame may contain one or multiple objects, or none at all
New objects may appear during the video and old objects disappear
Objects may only be partially visible when they enter or exit the frame
There may be overlapping bounding boxes for the same object
The same object will be in the video for multiple successive frames

To speed up the entire process, we only send each fully visible object to the defect detection model twice. We then average the probability output of the model and assign the label to that object permanently. This way we can save both computation time and money by not calling the model endpoint needlessly for the same object multiple times throughout the video.

Conclusion

Here is the result output video stream and an extracted frame from the quality control process. Blue means that the object has been detected but has not yet been classified because the object is not fully visible in the frame. Green means no defect detected and red is a defect:

The video can be found here.

These findings demonstrate that it is possible to develop an automated visual quality control pipeline with a minimal number of samples. In a real-world scenario, we would have access to much longer video streams and the ability to iteratively expand the dataset to enhance the model until it meets the desired quality standards.

Despite these limitations, thanks to Vertex AI, we were able to achieve reasonable quality in just the first training run, which took only a few hours, even with a small dataset. This highlights the efficiency and effectiveness of our approach of utilizing pretrained models and AutoML solutions, as we were able to achieve promising results in a very short time frame.

Please contact us for more information on how to utilize artificial intelligence in industrial solutions.