Metaflow

Metaflow is an open-source framework originally developed at Netflix to simplify the lifecycle of ML, AI, and data science projects. It provides a unified API for building, deploying, and managing workflows that span from local experimentation to cloud-scale production. The framework is designed for data scientists and ML engineers who need to move quickly without sacrificing reproducibility or operational rigor. With Metaflow, you write workflows as directed acyclic graphs (DAGs) in plain Python, with automatic versioning of data, code, and results. It handles data flow between steps, manages dependencies, and integrates with cloud services like AWS, Azure, GCP, and Kubernetes. Users can develop locally in notebooks, scale out using cloud resources (GPUs, multiple cores), and deploy to production with a single command. Recent releases introduce the spin command for incremental flow development, recursive and conditional steps for agentic workflows, and checkpointing for long-running tasks. What sets Metaflow apart is its emphasis on human-centric design: it treats the workflow as a first-class object, automatically tracks experiments, and enables seamless collaboration. The framework is battle-hardened at Netflix and used by hundreds of companies, from startups to enterprises, across industries like media, healthcare, and e-commerce. It supports advanced patterns like recursive and conditional steps, custom decorators, and event-driven deployments. Metaflow is self-hosted, requiring users to manage their own infrastructure, but offers sandbox environments for testing. It is not a no-code platform or a fully managed SaaS solution, making it best suited for teams that want full control over their MLOps stack.