What is MLflow?
An open source machine learning platform for managing the complete ML lifecycle, developed at Databricks, that includes four components supporting experimentation, reproducibility, deployment, and a central model registry.
Use cases for MLflow include:
Generative AI
Deep Learning
Traditional Machine Learning
Evaluation
Model Management
Use cases for MLflow include:
Generative AI
- Improve generative AI quality
- Build applications with prompt engineering
- Track progress during fine tuning
- Package and deploy models
- Securely host LLMs at scale with MLflow Deployments
Deep Learning
- Native integrations with popular DL frameworks (PyTorch, TensorFlow, Keras)
- Simple, low-code performance tracking with autologging
- UI for deep learning model analysis and comparison
Traditional Machine Learning
- End-to-end MLOps solution for traditional ML, including integrations with scikit-learn, XGBoost, and PySpark
- Simple, low-code performance tracking with autologging
- UI for model analysis and comparison
Evaluation
- Compare different ML models and GenAI application versions
- Evaluate different prompts
- Compare performance against a baseline to prevent regressions
- Simplify and automate performance evaluation
Model Management
- Package models for production, including code and dependencies
- Catalog, govern, and manage model versions
- Orchestrate model rollouts to staging and production
- Deploy models for large scale batch and real-time inference
Categories & Use Cases
Product Demos
Technical Details
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FAQs
What is MLflow?
An open source machine learning platform for managing the complete ML lifecycle, developed at Databricks, that includes four components supporting experimentation, reproducibility, deployment, and a central model registry.
How much does MLflow cost?
MLflow starts at $0.