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How to Build a Better AI For Your Business – with Hyun Kim

How to Build a Better AI For Your Business – with Hyun Kim

by Superb AI | Aug 22, 2022 | Uncategorized

Is it possible to make artificial intelligence more accessible to companies, both large and small? Our latest episode is with Hyun Kim, Co-Founder and CEO of Superb AI, that is aiming to solve this challenge and building a platform that aims to make shipping AI models...
Part 3: Building a DataOps Team for Your Computer Vision Projects

Part 3: Building a DataOps Team for Your Computer Vision Projects

by Superb AI | Jul 13, 2022 | Uncategorized

Introduction Common reasons behind computer vision projects failing are (1) a failure to make it to production, (2) the time where your coveted computer vision scientists and engineers spend too much of their time on menial tasks, and (3) increased governance risk. It...
How to Prioritize Data Quality for Computer Vision: An Expert Primer

How to Prioritize Data Quality for Computer Vision: An Expert Primer

by Superb AI | Jun 2, 2022 | Uncategorized

With the rise of the data-centric AI movement (of which computer vision is a subset), the spotlight has been shifting from algorithm design to dataset development. Data is the highest contributor to model performance for many modern neural network architectures....
An Introduction to Image Classification [+ Superb AI Tutorial]

An Introduction to Image Classification [+ Superb AI Tutorial]

by Superb AI | Apr 29, 2022 | Uncategorized

Introduction In the world of computer vision, accuracy is critical. If your model isn’t detecting images correctly, then its application in the real world can be rendered useless, or worse, dangerous. A computer vision model that incorrectly identifies objects can...
Part 2: Three DataOps Challenges That Most Computer Vision Teams Struggle With

Part 2: Three DataOps Challenges That Most Computer Vision Teams Struggle With

by Superb AI | Apr 18, 2022 | Uncategorized

This is a 3 part series. To view Part 1 click here.  Introduction Implementing state-of-the-art architectures, tuning model hyperparameters, and optimizing loss functions are the fun parts of machine learning. Sexy as it may seem, behind each model that gets deployed...
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