MLOps with MLflow: Using MLflow Projects & Recipes

MLflow Projects enable you to package machine learning code data and environment specifications for reproducibility and easy sharing. Registering projects in MLflow simplifies version control and enhances collaboration within data science teams. MLflow Recipes on the other hand automate and standardize machine learning tasks with pre-defined templates and configurations promoting consistency and repeatability while allowing customization for specific applications. With recipes and projects combined MLflow becomes a powerful tool for impactful and consistent results streamlining data science workflows. You will start this course by learning how MLflow Projects enable you to package share and reproduce machine learning code. Next you will learn about MLflow Recipes that automate machine learning tasks in reproducible environments. You will explore the MLflow Regression Template customize its files for model trAIning and run the recipe to view the model s performance. Finally you will explore running a classification recipe in Databricks and modifying YAML and code files for configuration.