Metaflow is an open-source framework designed to streamline the development and management of real-world machine learning, AI, and data science projects. Originally developed at Netflix to support demanding production workloads, it enables engineers and data scientists to build robust workflows using plain Python.
Key Features
- Modeling: Use any Python library for models and business logic, with built-in library management for local and cloud environments.
- Deployment: Deploy workflows to production with a single command, integrating smoothly with existing systems.
- Versioning: Automatic tracking and storage of variables within flows for easy experiment tracking and debugging.
- Orchestration: Create, test, and debug workflows locally, then deploy to production without code changes.
- Compute: Scale out to the cloud on demand, utilizing GPUs, multiple cores, and large memory instances.
- Data: Access data from warehouses and flow it across steps with automatic versioning.
Who It’s For
Metaflow is built for ML/AI engineers and data scientists who need to move from experimentation to production efficiently.
Workflow
- Develop and explore locally using notebooks and Metaflow’s APIs, with results stored and versioned automatically.
- Scale out to the cloud for large-scale execution, team collaboration, and parallel computing.
- Deploy final workflows to production with a single click, enabling event-driven, real-time, or batch processing.
Cloud Support
Metaflow integrates with AWS (EKS, Batch, Step Functions, Trainium), Azure (AKS, Blob Storage), Google Cloud (GKE, Cloud Storage), and custom Kubernetes clusters, allowing you to bring your own cloud infrastructure.
Key Benefits
- Open-source and free to use.
- Battle-hardened at Netflix for real‑life ML/AI projects.
- Seamless transition from local development to cloud scale.
- Automatic experiment tracking and versioning of all artifacts.
