We’ve covered episodes about AI in logistics in the past. Let’s now focus our attention to manufacturing. Some AI applications in this sector include predictive maintenance, quality assurance using computer vision, anomaly detection, and digital twins.

Building these solutions takes time and requires accuracy. If developed and operated manually 100%, this approach risks making more errors in your ML models and datasets. Rather than relying on grit, we can automate the entire process and let the AI solution run like clockwork.

MLOps (machine learning operations) automates the ML process together with the help of a feedback loop system. It can be divided into layers, like a layered cake. Some of its layers include experiment tracking, dataset monitoring, qualitative tests and explainability layer.

In this episode, we talk to Marek Tatara, Chief Scientific Officer of DAC.digital as he tells us more about how MLOps works, and their experience in building a customized MLOps for the semiconductor industry under a large cooperative EU-funded project.

Listen to our episode if you want to make your ML project more effortless and more reliable at a larger scale.

Who is Marek Tatara?

Marek Tatara, PhD - Chief Scientific Officer and Tech Lead of the AI team at DAC.digital, Assistant Professor at Gdańsk University of Technology, AI/ML Expert at M5 Technology, Member of the Polish Society For Measurement, Automatic Control And Robotics. At DAC.digital, he works on the research agenda of the company and on the implementation of both EU-funded and commercial R&D projects from the field of Computer Vision (especially multi-camera setup, 3D reconstruction, and object detection and tracking), Machine Learning (mainly for computer vision, e.g., object detection, DNN optimization, semantic segmentation), and Embedded Systems (wireless communication for IoT devices and medical devices implementation.

Where to find Marek:

• DAC.Digital website

• LinkedIn

Resources:

• Book recommendation: Modern Control Theory by William Brogan

• Aims50 - Artificial Intelligence in Manufacturing leading to Sustainability and Industry 5.0

Time Stamps

(00:00:00) Trailer

(00:01:08) Who is Marek Tatara?

(00:01:50) AI in Manufacturing: Applications

(00:04:18) Concepts behind MLOps

(00:08:18) Custom vs pre-made tools for MLOps

(00:10:41) Building an MLOps project is like building a layered cake

(00:17:08) Adopting MLOps among small and medium manufacturing companies

(00:19:26) Working in an EU funded ML project

(00:21:06) Advice on AI adoption and implementation for SMEs
(00:26:11) Final remarks and book recommendations

--- More on G.M.S.C. Consulting

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Music credits: storyblocks.com

Logo credits: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Joshua Coleman, Unsplash⁠⁠⁠

Does your business need an AI computer vision co-pilot?

You might need it, especially if you’re working on a high-stake AI project that requires precision or accuracy.

In this episode, Akridata’s AI engineer Alexander Berkovich tells us more about it as he covers the different use cases that have used Akridata’s computer vision co-pilot. To name a few are corrosion detection, autonomous vehicles, railroad inspection, and industrial maintenance.

You can learn more about good data management and deployment practices to ensure a less biased operating AI model for your computer vision project.

Who is Alexander Berkovich?

Alexander is a principal AI/ML engineer at Akridata, whose tools and services save time and lower costs developing vision-based applications and systems. Previous positions include an R&D manager, team lead, and algorithm developer in a variety of domains, ranging from smart cities, to medical quality inspections, manufacturing and more, all in the computer vision space. His aim is to automate decision making based on a combination of visual sensors, software, hardware and the maths behind it all, to improve the quality of services, products and daily life.

In addition to focusing on the technical aspects of development, Alex advocates for the importance of grasping the business case and employing high-quality data, especially in this AI driven era.

Where to find Alexander:

• Akridata.ai

• LinkedIn

Time Stamps

(00:00:00) Trailer

(00:01:42) About Alexander and Akridata

(00:03:42) About computer vision copilots

(00:07:15) Use cases

(00:21:50) Importance of data quality when training models

(00:16:01) Model training and deployment, accuracy, precision and recall

(00:20:58) Dealing with clients’ needs

(00:24:44) Addressing biases in AI computer vision models

(00:47:44) Closing remarks

--- More on G.M.S.C. Consulting

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Music credits: storyblocks.com

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Do you want to build the ultimate search engine that works perfectly for your business?

A multimodal system processes all types of information like images, videos, audio, and text. It can end up either as a Frankenstein or a Swiss knife depending on how clunky or how smoothly these models communicate with one another.

In this episode of the AI and Digital Transformation Podcast by G.M.S.C. Consulting, we had a chat with Duarte Carmo about building a customized multimodal search engine. We learned about his experience on building a multimodal search engine for a startup, and things you need to consider when building an AI product that does not rely heavily on big tech’s costly services.

If you like tinkering and using non-conventional methods to build personalized AI products like a multimodal search engine, this is the episode for you.

Who is Duarte Carmo?

Duarte is a Portuguese technologist who is now based in Copenhagen. He loves finding ways on making technology improve people’s lives. He solves problems end-to-end by combining his interests and work experience in Machine Learning, Data, Software Engineering, and People. If he’s not working on a project, you’ll find him learning about new gadgets, writing code, taking photos, or running.

Check out our ⁠show notes⁠ for more info on Duarte Carmo. ---

Time Stamps

(00:00:00) Trailer

(00:01:07) Who is Duarte?

(00:02:57) Use case: building a multimodal search engine

(00:08:23) Challenges of developing and using a multimodal search engine

(00:12:23) Human + machine: precision, reasoning and language

(00:16:34) Challenges of communicating between humans and machines: freedom vs restriction, bias vs. variance

(00:19:13) Why is it important to ask your client what their problem is?

(00:22:58) Building an AI product: iterative approach vs. perfect launch

(00:26:49) Priorities in AI development

(00:31:29) Are customized AI solutions affordable or too expensive for small and medium businesses?

(00:36:42) Privacy as a concern for multimodal search engines; APIs and privacy

(00:38:56) Duarte’s achievement of building a customized multimodal search engine

(00:40:14) Final remarks and book recommendations

--- More on G.M.S.C. Consulting

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Music credits: storyblocks.com

Logo credits: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Joshua Coleman, Unsplash⁠⁠⁠