Metaflow is an open-source framework created at Netflix and released publicly in 2019. It is designed to help data scientists and machine learning engineers build, execute, and manage real-world ML and data science projects. The framework provides abstractions for data access, versioning, computation, and deployment, allowing practitioners to move from local prototyping to large-scale cloud execution with minimal code changes. Metaflow supports Python and R, and integrates with major cloud providers including AWS, Azure, and Google Cloud. It handles workflow orchestration, experiment tracking, and resource management, enabling teams to run steps locally or on distributed cloud infrastructure. The project is maintained by Outerbounds and has an active open-source community. It is typically used alongside orchestration tools such as Apache Airflow and AWS Step Functions, and can be deployed on-premises or in the cloud.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- Self-hosted
- API
Key features
Use cases
- Build end-to-end ML pipelines
- Version and track ML experiments
- Scale computation to the cloud
- Deploy ML models to production
- Manage data science project dependencies
Best for
- Data scientists who need to build and iterate on ML workflows without managing infrastructure
- ML engineers who need to scale experiments from local prototypes to cloud production systems
- Data science teams who need reproducible, versioned pipelines across collaborative projects
Integrations
Automation platforms
Apache Airflow, AWS Step Functions, Argo Workflows
Developer
Kubernetes, AWS Batch, GitHub
Databases
Amazon S3, Azure Blob Storage, Google Cloud Storage
Other
AWS, Azure, Google Cloud