Metaflow

About Metaflow

Metaflow is a framework for building and managing real-life ML, AI, and data science projects. It supports modeling with any Python libraries, dependency management locally and in the cloud, workflow orchestration in plain Python, automatic variable tracking, cloud compute scaling, and data access from warehouses with versioning. Workflows can be developed and debugged locally and deployed to production with a single command, with integration to other systems through events.

For scaling, Metaflow can use GPUs, multiple cores, large memory, and parallel instances. The homepage says users can deploy the Metaflow stack on their own cloud account or an on-premise Kubernetes cluster; supported options include AWS, Azure, Google Cloud, and custom Kubernetes. The homepage also says it integrates with existing infrastructure, security, and data governance policies. Pricing is not stated, and no geographic eligibility or usage limits are stated.

Key features

• Use any Python libraries for models and business logic
• Deploy workflows to production with a single command
• Track and store flow variables automatically for experiment tracking and debugging
• Create robust workflows in plain Python and debug them locally
• Scale cloud compute with GPUs, multiple cores, memory, and parallel instances
• Access data from data warehouses and version data across steps

Metaflow at a glance