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Functions triggered upon the successful completion or failure of a workflow for cleanup or notifications.
Distinct from Workflow Logic: Shortlist candidates focus on notification delivery or registry management rather than the lifecycle hook mechanism itself.
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Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It
Provides hooks to execute specific cleanup functions or send notifications when a workflow completes or fails.