The Model Review - Improving Transparency, Reproducibility, and Knowledge Sharing

Discover how to implement a Model Review process for Machine Learning (ML) models in this insightful PyCon US talk. Learn about the importance of transparency, reproducibility, and knowledge sharing in ML development. Explore MLflow, a powerful Python package that simplifies and automates experiment tracking. Gain practical insights into implementing a Model Review process for production ML models, and see how MLflow can streamline workflows and facilitate team learning. Understand the parallels between Code Review and Model Review, and explore real-world examples of autologging model training. By the end of this talk, you'll have a clear understanding of how to improve your ML development process through effective Model Review practices.

The Model Review
Motivation
Why do we Review Code?
First Example
Autologging Model Training
Step 4: Model Review