MLflow

MLflow is an open-source platform designed to streamline the end-to-end machine learning (ML) lifecycle. It provides a set of tools for managing experiments, packaging ML code for reproducibility, and deploying and managing ML models. Its core components include MLflow Tracking (for logging parameters, code versions, metrics, and artifacts), MLflow Projects (for packaging ML code in a reusable and reproducible format), MLflow Models (for managing model formats and deployment to various environments), and MLflow Model Registry (for collaborative model management). MLflow is language-agnostic and can be used with any ML library and on any cloud. It aims to solve common challenges in ML development, such as tracking experiments, reproducing results, and deploying models consistently, making the entire ML workflow more efficient and organized.