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Experiment tracking, model registry & production monitoring for ML teams
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Unified platform for tracking, evaluating, and deploying AI models
MLflow provides end‑to‑end experiment tracking, observability, prompt management, evaluation, and model registry, enabling data scientists and GenAI developers to build, compare, and deploy AI applications confidently.
MLflow is a developer‑focused platform that brings together experiment tracking, model registry, and deployment tools for both traditional machine learning and generative AI workloads. It supports Python, Java, R, and TypeScript/JavaScript, and integrates natively with popular frameworks such as scikit‑learn, LangChain, and OpenAI.
The platform offers unified experiment tracking with automatic logging of parameters, metrics, and artifacts, as well as LLM‑specific tracing and observability. Built‑in evaluation suites let you benchmark question‑answering or other generative models, while prompt versioning ensures consistency across teams. A centralized model registry and deployment utilities simplify moving models to Docker, Kubernetes, Azure ML, AWS SageMaker, or other environments.
MLflow can run locally, on‑premise, or in the cloud, and is available as a managed service from major providers like Amazon SageMaker, Azure ML, and Databricks. This flexibility lets organizations adopt the platform at any scale while retaining full control over their infrastructure.
When teams consider MLflow, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Experiment tracking for scikit‑learn models
Automatic logging of parameters, metrics, and artifacts enables easy comparison across runs.
LLM prompt versioning
Maintain consistent prompts across multiple agents and teams, improving collaboration and reproducibility.
Model evaluation for question‑answering
Generate standardized metric reports to compare model performance over time.
Production deployment on Kubernetes
Containerize and serve models with MLflow’s deployment tools, integrating with existing CI/CD pipelines.
Run `pip install mlflow` to install the Python package.
MLflow provides SDKs for Python, Java, R, and TypeScript/JavaScript.
Yes, it can run locally, on‑premise, or in any cloud environment.
Managed services are offered by Amazon SageMaker, Azure ML, Databricks, and others.
Call the appropriate `mlflow.<library>.autolog()` (e.g., `mlflow.openai.autolog()`) before running your model.
Project at a glance
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