MLOps / Pipelines and controlled delivery

MLOps β€” Pipelines and controlled delivery

Dependencies, reusable evidence, acceptance, version selection, delivery patterns, feature parity, and exposure.

6 lessons Β· No account required

What you will learn

  • Dependencies, reusable evidence, acceptance, version selection, delivery patterns, feature parity, and exposure.

  1. Pipeline DAGs, caching, and idempotency

    Represent true dependencies and define what retries or cache reuse may repeat safely.

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  2. Evaluation slices and acceptance gates

    Turn a candidate comparison into explicit evidence without letting one favorable average erase important failures.

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  3. Registry versions, aliases, and promotion

    Distinguish immutable artifact identity from mutable selection and preserve the evidence behind a change.

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  4. Batch and online delivery contracts

    Preserve prediction identity, completion semantics, and latency meaning across delivery patterns.

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  5. Training-serving skew and feature time

    Compare the actual historical and delivered feature representations, including when each value became usable.

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  6. Rollouts, shadowing, and rollback

    Plan bounded exposure and recovery around the full prediction contract, not only the model file.

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