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DataFreeOpen Source
FLYTE
AI orchestration platform for data, ML, and compute workflows
Apache-2.0
ABOUT
Production ML and data workflows involve complex multi-step pipelines — data ingestion, feature engineering, training, evaluation, deployment — that are brittle when stitched together with scripts and ad-hoc scheduling. Flyte provides a unified orchestration platform where teams define workflows as typed Python functions, then execute them on Kubernetes with automatic retries, caching, versioning, and observability. It eliminates the gap between development and production by letting data scientists write workflows in Python while DevOps gets reliability, scalability, and audit trails out of the box.
INSTALL
pip install flytekitINTEGRATION GUIDE
1. Orchestrate ML training pipelines with automatic caching so that unchanged steps are skipped on re-runs, saving compute costs
2. Build reproducible data processing workflows with typed inputs, outputs, and versioned executions for compliance and audit
3. Coordinate multi-step feature engineering, model training, evaluation, and deployment as a single versioned DAG
4. Run batch inference workflows at scale on Kubernetes with automatic parallelization and spot instance handling
TAGS
data-pipelinemlopsorchestrationworkflowkubernetespythonmachine-learning