mlflow

**MLflow** is the **open-source MLOps platform for experiment tracking, model packaging, and model registry governance** - it helps teams maintain reproducibility and controlled model promotion from research to production. **What Is MLflow?** - **Definition**: Framework for logging parameters, metrics, artifacts, and lineage for ML runs. - **Key Components**: Tracking server, model registry, project packaging, and deployment integration options. - **Workflow Role**: Centralizes run metadata and model versions across experiments and teams. - **Ecosystem Fit**: Integrates with popular frameworks and storage backends in cloud or on-prem setups. **Why MLflow Matters** - **Reproducibility**: Preserves run context needed to rerun and validate model results. - **Model Governance**: Registry stages support controlled promotion and rollback decisions. - **Team Collaboration**: Shared experiment history reduces duplicated work and confusion. - **Auditability**: Logged lineage improves compliance and change-trace requirements. - **Operational Transition**: Bridges the gap between experimentation and production deployment workflows. **How It Is Used in Practice** - **Tracking Standard**: Enforce consistent run logging schema for parameters, metrics, and tags. - **Registry Policy**: Define promotion criteria and approval gates for staging and production transitions. - **Artifact Integration**: Connect MLflow tracking to durable artifact stores with lifecycle policies. MLflow is **a practical control plane for experiment and model lifecycle management** - standardized tracking and registry workflows improve reproducibility and deployment reliability.