Harnessing AI Power: Building the Next Generation 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.  

 

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Smart Operations: Embracing AI for Efficiency and Growth

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.  

 

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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:

  1. Have you analyzed the right areas to invest in for leveraging AI in terms of your competitiveness?
  2. If your AI strategy = ChatGPT, what kind of analysis is it based on?
  3. 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?
  4. 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:

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.

 

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

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.

 

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Top 4 Picks by Codento Team –  fooConf, Helsinki

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.

 

#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:

 

#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!

 

 

Contact us for more information about Software Intelligence services:

 

Introduction to AI in Business Blog Series: Unveiling the Future

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.  

 

 

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Google Cloud Nordic Summit 2023: Three Essential Technical Takeaways

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.

 

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100 Customer Conversations Shaped Our New AI and Apps Service Offering 

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:

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.

 

Contact us for more information on our services:

 

AI in Manufacturing: AI Visual Quality Control

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:

  1. Train a model powered by Google Vertex AI AutoML
  2. Use the prebuilt Google Cloud Vision API
  3. 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.

 

 

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.

 

 

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