Increase your computer vision project’s reliability using an AI copilot | Alexander Berkovich

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

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