Buy AND Build for Production Machine Learning with Nir Bar-Lev

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About this Episode

Today we're joined by Nir Bar-Lev, co-founder and CEO of ClearML. In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, and his thoughts on why experiment management has become an automatic buy, be it open source or otherwise. We also discuss the disadvantages of using a cloud vendor as opposed to a software-based approach, the balance between MLOps and data science when addressing issues of overfitting, and how ClearML is applying techniques like federated machine learning and transfer learning to their solutions.
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Thanks to our sponsor Allegro AI

Allegro AI helps companies develop, deploy and manage deep learning-based solutions with a focus on unstructured data for perception. Our customers span diverse industries and use cases from automotive, drones, radiology & healthcare to security, manufacturing, and silicon. With Allegro AI, businesses are able to bring to market and then manage higher quality products, faster and more affordably. Allegro AI's customers include global leaders that span the automotive, media, medical, security, construction and other industries.
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