Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
Published:Feb 17, 2020 22:02
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Emmanuel Ameisen, a machine learning engineer at Stripe and author of "Building Machine Learning Powered Applications." The discussion focuses on practical aspects of building ML-powered products, covering project structuring, debugging, model explainability, different model types, and post-deployment monitoring. The episode likely provides valuable insights for machine learning practitioners and those interested in the productization of ML models. The focus is on the practical application of ML, moving beyond theoretical concepts.
Key Takeaways
- •Focus on practical aspects of building ML-powered products.
- •Discussion includes project structuring, debugging, and model explainability.
- •Covers different model types and the importance of post-deployment monitoring.
Reference
“The article doesn't contain a direct quote, but the core topic is about structuring end-to-end machine learning projects, debugging and explainability, model types, and post-deployment monitoring.”