Metaflow.org

Features

• Python Library Integration: Utilize any Python libraries for modeling and business logic, both locally and in the cloud.
  
• Seamless Deployment: Deploy workflows to production with a single command, integrating effortlessly with surrounding systems.
  
• Automatic Versioning: Tracks and stores experiment variables automatically for easy tracking and debugging.
  
• Robust Orchestration: Develop workflows in plain Python and deploy transitions with no changes required.
  
• Cloud Compute Power: Leverage cloud resources to execute functions at scale, from GPUs to multiple cores.
  

Use Cases:

• Machine Learning Engineers: Manage complex ML workflows, ensuring seamless integration into existing infrastructures.
  
• Data Scientists: Efficiently iterate and deploy data models and experiments with scalable cloud resources.
  
• Production Deployment: Deploy production-ready versions of models automatically reacting to updated data events.
  
• Cross-Cloud Deployment: Deploy Metaflow configurations across major cloud services like AWS, Azure, and Google Cloud.
  
• Collaboration: Organize projects for easy collaboration among team members, ensuring version control and resource management.
  

Originally developed at Netflix, Metaflow offers a battle-tested framework for building, deploying, and managing real-world ML and AI projects effectively.