Package a validated prediction pipeline, benchmark the real serving path and prepare controlled rollout, monitoring and recovery evidence.
Record input schema, preprocessing, output semantics, supported shapes and failure behavior for the actual user task. Define quality and service objectives, privacy boundaries and fallback behavior with owners. Keep offline model evaluation distinct from the latency and reliability of the complete serving service.
Save the model, preprocessing, vocabulary or feature schema, dependencies and reference inputs/outputs as one versioned artifact. Load it in the intended serving environment and compare reference predictions, including missing or malformed inputs. Record any nondeterminism or tolerance that the task actually permits.
If testing quantization, pruning or compilation, compare complete artifact sizes consistently and check representative quality and failure slices. Inventory actual resident weights, caches, activations and runtime memory per device. A smaller artifact or lower weight precision does not by itself establish a faster or cheaper service.
Measure preprocessing, model work, postprocessing, queueing and transport with compatible boundaries. Collect actual latency observations and completed/failed counts under representative request shapes and offered load. Use observed percentile and request-rate modes; track pending work, timeouts, warmup and batching separately rather than inventing P99 from an average.
Choose a rollout scope and evaluation duration based on expected traffic and decision risk. Verify that any shadow path has no unintended user-visible side effects, and define the evidence needed before increasing exposure. Test a rollback or fallback with compatible model/schema versions and retain the previously working artifact.
Monitor latency, traffic, errors, saturation, data quality and task-specific outcome evidence with defined owners and responses. Compare drift with actual performance and input changes; a generic drift threshold is not an automatic retraining decision. Save the release version, evaluation evidence, recovery instructions and next review trigger.
Format a copied JSON example for inspection; this does not query, validate a live API contract or configure a server.
Write and download the task brief, measured results and handoff decisions as Markdown.
Compare actual artifact sizes without inferring latency or quality improvement.
Build a per-device resident-memory ledger with explicit state components, budget and reserve.
Summarize actual latency observations or completed-request counts; keep serial stage assumptions separate.
Loading your checklist…
Scikit-learn explains why the fitted transformation used for training must also be applied consistently during prediction.
Google SRE describes useful latency, traffic, error and saturation measurements, including attention to latency distributions rather than averages alone.
Keep the following evidence with the actual version used for this task.
| Record | Include | Check |
|---|---|---|
| Artifact | Model, preprocessing, schema and reference outputs | Load in the intended serving environment |
| Benchmark | Workload, timing boundary, tails and errors | Include queueing and pending requests |
| Recovery | Previous version, compatibility and trigger | Rehearse before expanding exposure |
Investigate the changed population or pipeline and delayed outcome evidence before choosing a response.
A notebook forward-pass time excludes much of a real user request.