Metaflow

Pythonic Workflows and DAGs

Write flows as ordinary Python decorated with steps, retries, and branching. Local runs feel like scripts, but the same code executes as a managed workflow when promoted to shared infrastructure. Parameters expose tunables cleanly, and unit-friendly structure helps teams review logic without a new DSL. Foreach branches fan work out efficiently, resume-from-step shortcuts speed recovery, and testable, parameterized steps keep reviewers focused on intent, not boilerplate.

Data Versioning and Lineage

Each run snapshots code, parameters, and outputs so lineage is explicit. You can reproduce a past result or inspect differences between two candidates without hunting through ad-hoc folders. Artifacts keep models and datasets attached to the exact run that created them, clarifying ownership and audit trails later. Content-addressed stores avoid overwriting good results, and run diffs show parameter changes next to metric deltas with links back to notebooks or commits.

Scaling and Compute Profiles

Allocate CPU, memory, GPU, and timeouts per step; long jobs move to containers or batch while quick steps remain local for speed. Retries and caching reduce wasted compute and shorten feedback loops during tuning. Profiles capture typical resource needs for reuse, and queues smooth bursts so shared clusters remain responsive. Reusing intermediate results turns heavy preprocessing into a one-time cost shared across related experiments safely and predictably.

Results, Cards, and Observability

Summarize outcomes with rich cards that display metrics, charts, and key artifacts for stakeholders. Logs and metadata make it simple to reason about behavior when a run degrades or diverges from expectations. Cards render to shareable HTML for reviewers without notebook access, while alerts flag drift or anomalies quickly. Drilldowns expose raw artifacts alongside charts so teams validate that improvements reflect real signal instead of noise or accidental leakage.

Productionization and Governance

Schedule flows, parameterize by environment, and ship with CI checks so changes roll out safely. Human-in-the-loop steps collect approvals before promotion to production endpoints or dashboards. Tags and roles keep ownership clear, while alerts escalate failures quickly so issues don’t linger unnoticed. Staged environments separate dev, staging, and production; secret handling keeps credentials out of code; and promotion checklists enforce tests and sign-offs consistently.