Introduction
MLflow is an open-source platform designed to manage the end-to-end lifecycle of ML models, enabling teams to track experiments, package code, register models, and serve them in production with consistency and control. MLflow provides a modular framework that integrates seamlessly with your ML stack, offering clear separation of concerns across experimentation, deployment, and governance. It serves as a single source of truth for model metadata and performance, helping teams move from notebooks to production without losing observability or alignment.
Key benefits of using MLflow include:
Experiment Tracking: Logs parameters, metrics, artifacts, and source code for each training run—enabling reproducibility, comparison, and auditability across models and datasets.
Model Registry: Manages model versions, stages (e.g., staging, production), approvals, and lineage—providing governance and lifecycle management for production ML.
Flexible Deployment Options: Supports model deployment to REST endpoints, batch inference jobs, or third-party platforms such as KServe, SageMaker, Ray Serve, or MLServer.
Framework-Agnostic Integration: Works with popular ML frameworks like scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face Transformers, and custom Python code.
Artifact and Environment Management: Stores serialized models, preprocessing logic, and environment definitions—ensuring consistent deployment and compatibility across environments.
MLflow is used across a wide range of workflows including experiment tracking for RAG pipelines, classification model development, embedding models, and evaluation systems. It is tightly integrated with orchestration tools like Airflow, PipeCat, and Kubeflow Pipelines, and connects to model serving frameworks like Ray Serve and KServe for deployment. By adopting MLflow, you can ensure its ML lifecycle is transparent, reproducible, and production-ready—empowering teams to iterate quickly while maintaining operational excellence and governance.