Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488
Published:May 31, 2021 17:54
•1 min read
•Practical AI
Analysis
This podcast episode from Practical AI features Nir Bar-Lev, CEO of ClearML, discussing key aspects of production machine learning. The conversation covers the evolution of his perspective on platform choices (wide vs. deep), the build-versus-buy decision for companies, and the importance of experiment management. The episode also touches on the pros and cons of cloud vendors versus software-based approaches, the interplay between MLOps and data science in addressing overfitting, and ClearML's application of advanced techniques like federated and transfer learning. The discussion provides valuable insights for practitioners navigating the complexities of deploying and managing machine learning models.
Key Takeaways
- •The build vs. buy decision is a key consideration for companies deploying machine learning.
- •Experiment management is becoming a standard requirement.
- •Software-based approaches may offer advantages over cloud vendor solutions in certain scenarios.
Reference
“The episode explores how companies should think about building vs buying and integration.”