https://hub.imt-atlantique.fr/datascience-toolkit/session2/practical_4/index.html Duration2h30 AI Banned Introduction This practical session introduces MLflow for systematic experiment tracking and model management in machine learning pipelines. You’ll learn to transform your structured air quality pipeline into a fully tracked and reproducible ML workflow that follows industry best practices for experiment management. Through this hands-on workshop, you will master essential MLOps skills: Experiment Tracking: Systematically log parameters, metrics, and artifacts for all ML experiments Model Management: Version and organize trained models with metadata and lineage tracking Reproducibility: Ensure experiments can be reproduced and compared reliably Collaboration: Share experiment results and models across team members Production Readiness: Prepare models for deployment with proper versioning and metadata MLflow is the industry standard for ML experiment tracking, used by companies like Databricks, Netflix, and many organizations to manage their ML lifecycles. Learning MLflow prepares you for real-world ML engineering roles where experiment tracking and model management are critical. Hugo en-us