MLflow

MLflow is the industry-standard open-source platform for managing the end-to-end machine learning lifecycle, originally created by Databricks in 2018 and now one of the most widely adopted MLOps tools in the world with over 18 million monthly downloads and contributions from more than 800 developers. MLflow provides a comprehensive set of tools for experiment tracking, model packaging, model registry, and deployment that have become foundational to how organizations build, manage, and govern their ML systems. MLflow's core components include MLflow Tracking for logging experiments (parameters, metrics, artifacts, and code versions), MLflow Projects for packaging ML code in reproducible formats, MLflow Models for packaging models in a standard format that supports multiple deployment targets, and the MLflow Model Registry for collaborative model lifecycle management including versioning, staging, and approval workflows. The Model Registry is particularly relevant for AI governance, as it provides a centralized repository where models are registered, versioned, annotated with descriptions and tags, and moved through stage transitions (Staging, Production, Archived) with approval workflows. From a governance perspective, MLflow provides essential building blocks: experiment lineage tracking ensures reproducibility and auditability, the model registry enforces lifecycle controls, model signatures document expected input/output schemas, and integration with MLflow Evaluate enables model quality assessment. Recent versions have expanded governance-relevant features including enhanced model versioning, improved access controls in Databricks-managed MLflow, and support for model documentation through model cards and annotations. MLflow's open-source nature and massive community are significant strengths. The platform integrates with virtually every ML framework (TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face), cloud platform (AWS, Azure, GCP), and deployment target, providing vendor-agnostic lifecycle management. Databricks also offers a fully managed version with additional enterprise features including fine-grained access controls, audit logging, and Unity Catalog integration for comprehensive data and model governance. However, MLflow's governance capabilities are relatively basic compared to purpose-built AI governance platforms. It provides the infrastructure for tracking and managing models but lacks built-in bias detection, fairness assessment, regulatory compliance mapping, or risk classification features. Organizations typically need to complement MLflow with additional governance tooling for comprehensive AI governance programs.