Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML.
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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.
Thanks to our sponsor!
Thanks to our friends at ClearML for their support of the TWIMLcon and today’s episode of the podcast! ClearML is a comprehensive open-source tool for managing ALL of your MLOps workflows within a unified and robust platform, providing an end-to-end solution for machine learning operationalization across your different departments and teams.ClearML offers collaborative experiment management, powerful orchestration, easy-to-build data stores, one-click model deployment and more, and listeners of today’s podcast can get started for free with ClearML by visiting twimlai.com/clearml.
Connect with Nir!
- AI Infrastructure Alliance
- TWIMLcon 2021: ClearML: Your R&D on MLOps! From zero to hero in two lines of code
- TWIMLcon 2021: Build, Buy And The Golden Ratio: How Theator Scaled Up Its Continuous Training Framework
- NVIDIA Transfer Learning Toolkit