Workshop on tracking and configuring your Machine Learning experiments

Presentation

Code

A deep dive into automating experiments to achieve both reproducibility and extendability in machine learning research. This session will explore MLFlow, a powerful library for experiment tracking, and cover techniques for configuring experiments and optimizing hyperparameters. Learn how to create machine learning science that not only delivers deterministic reproducibility but also yields generalizable results.

• Strategies for reproducible and extendable research
• In-depth exploration of MLFlow for experiment tracking
• Techniques for flexible experiment configuration and hyperparameter optimization

Who Should Attend: This workshop is tailored for experienced Python users familiar with training machine learning models. While the techniques presented are broadly applicable to any Python-based framework, PyTorch will be used as the primary example.