Overview
MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. In 2026, MLflow remains the industry standard for vendor-agnostic MLOps, having pivoted heavily into Generative AI (GenAI) capabilities through its MLflow Deployments and LLM Evaluation suites. Technically, it is architected as a set of REST APIs and a Python-first client library that interacts with two main storage components: an SQL-backed database for metadata (tracking server) and a blob-storage system (S3/GCS/Azure Blob) for model artifacts. Its modular design allows it to integrate seamlessly with any ML library (PyTorch, TensorFlow, Scikit-learn) and deployment target (Kubernetes, AWS SageMaker, Azure ML). The 2026 market position is solidified by its 'AI Gateway' functionality, which provides a unified interface for interacting with various LLM providers (OpenAI, Anthropic, MosaicML), allowing organizations to centralize security, credential management, and usage monitoring for large-scale enterprise AI deployments. As part of the LF AI & Data Foundation, it ensures a future-proof, community-driven ecosystem without vendor lock-in.