flyte

Why we included this project

Flyte is for teams that need to run multi-step ML pipelines in production rather than just prototypes; it treats workflow execution as a durable runtime, not a thin wrapper around scripts. You write plain Python tasks with @env.task, chain them, and the system handles scheduling, retries, and parallel fan-out across machines, including async workflows that run many steps concurrently. It also covers model serving, so the same code path that builds a pipeline can expose a FastAPI endpoint for predictions. As a graduated LF AI & Data project that is Kubernetes-native, it's built for scale where jobs are monitored, recovered after failures, and shared across a team, which is often the gap between a working notebook and a reliable service. Whether you're a solo researcher or a platform team, what matters is how much operational maturity you need before committing.