UNIT 016 h allocated
Dataset and artefact lineage
- Git versus data bytes
- DVC pointers and remotes
- checksums
- recovery
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MLOPS & CLOUD / CORE
MLO410MLOps: versioning, experiments, pipelines & monitoring
24 h productive allocation9 h 36 m learning + 14 h 24 m training4 units · 1 assessed practical brief
Detailed course plan and training brief. A complete taught course pack is in preparation. The linked foundation module, where shown, is a smaller available resource.
BEFORE YOU BEGIN
A clear starting point.
Prerequisites: MLO401
Core studio / CPU-friendly local or notebook environment
WHAT YOU SHOULD BE ABLE TO DEMONSTRATE
- Explain the assumptions and failure modes in dataset and artefact lineage.
- Complete the practical: connect data validation, training, evaluation and packaging into a reproducible pipeline; record an experiment and rehearse recovery from a bad release.
- Defend the resulting versioned dataset manifest with reproducible evidence.
THE SUBJECTS / FOUR CONNECTED UNITS
What you will study.
UNIT 026 h allocated
Experiment discipline
- MLflow parameters, metrics and artefacts
- run comparison
- registration and approvals
UNIT 036 h allocated
Pipeline orchestration
- Explicit dependencies
- idempotent stages
- validation gates
- retries
UNIT 046 h allocated
Operational learning
- Latency and failure rates
- data and concept drift
- feedback delay
- rollback evidence
THE TRAINING STUDIO
Make the subject
visible in your work.
Connect data validation, training, evaluation and packaging into a reproducible pipeline; record an experiment and rehearse recovery from a bad release.
Evidence to produce
- Versioned dataset manifest
- experiment ledger
- pipeline definition
- incident and recovery note
Training brief for supervised delivery. Code, datasets, answer keys and environment acceptance must be completed in the full course pack before delivery is confirmed.
ASSESS THE REASONING
A question you
should be able to answer.
Why should an improved offline metric not automatically promote a model to production?
Review dimensions
Technical correctness · reproducibility · interpretation of results · failure analysis · individual explanation. Course-level rubric and faculty moderation form part of the final adopted teaching pack.
External reference access, hardware, cloud accounts and paid services are separate. No third-party certification is included by implication.