MLOps / Prediction and evidence foundations

MLOps β€” Prediction and evidence foundations

Task ownership, schema quality, information boundaries, lineage, replay inputs, and complete packages.

6 lessons Β· No account required

What you will learn

  • Task ownership, schema quality, information boundaries, lineage, replay inputs, and complete packages.

  1. Task ownership and lifecycle contracts

    Define the prediction, decision, accountable roles, and evidence that must survive the model lifecycle.

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  2. Data schemas and quality gates

    Validate meaning and eligibility at the data boundary without disguising policy changes as cleaning.

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  3. Splits, availability, and leakage boundaries

    Make evaluation manifests reproduce the information actually available to training and prediction.

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  4. Experiment records and lineage

    Connect artifacts, executions, metrics, and selection decisions so a result can be investigated later.

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  5. Reproducible training boundaries

    Define what a training rerun must preserve and how its input manifest explains the resulting artifact.

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  6. Model packages and inference contracts

    Package the complete prediction function and verify compatible inputs before interpreting its output.

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