JAX Pathways

Course · Workload operationsPhase 16 · Study guide

Can you detect, explain and safely recover a failed model release?

Systems Specialization · CPU ReferenceCPU reference; target qualification separate8 lessons (~11.3h)

Revision v0.7.0 ·

Operational evidence connects work done to user-visible behavior. Separate worker health, model quality and release ownership so an alert leads to a specific investigation rather than automatic retraining.

Preparation: Distributed training

Runtime: Executed local CPU processes; accelerator and cluster transfer require separate validation

Start the phaseSee the project

Tools, project folders and experiment organization: prepare a run directory and keep its configuration and evidence together.

Check your starting point

A worker process is alive but has emitted no progress for a long time. Is it healthy?

Compare your reasoning

Liveness only establishes that the process exists or responds. Check readiness, last completed work, expected duration and checkpoint freshness before choosing a bounded timeout or recovery action.

If this is unfamiliar, work through Health, logs, and workload observability before attempting the project.

Build in stages

1. Observe and recover real work

Launch a bounded worker, retain structured events, rehearse a failure and verify the next restored update against uninterrupted work.

2. Control change and capacity

Reject invalid artifacts without changing selection. Revalidate rollback targets and calculate demand, reserve and cost with consistent measured units.

3. Connect MLOps, LLMOps and ModelOps

Use data and slice-quality gates, trace a versioned text application and bind review to the complete release bundle. Keep evaluator replay separate from live-model quality.

Try a changed condition

Make a prediction before opening the reasoning. Keep your answer and the evidence you would collect.

An input-distribution alert fires while delayed labels have not arrived. Should the system automatically retrain and promote?

Compare an approach

No conclusion about quality follows yet. Check data integrity, affected slices and service health; gather outcome evidence under a declared response policy. Any candidate must pass independent release gates, and selection must remain unchanged after failure.

Diagnose a likely failure

Symptom: A release passes average error while a rare critical slice fails.

Check next: Require per-slice counts and quality thresholds. Do not average slice means equally or treat a missing critical slice as a passing zero.

Decide what is ready

Use workload-operations and engineering-release. Keep a real failure/recovery timeline, failed-gate invariance, a rollback drill, run/model identity and named review responsibilities.

Where to put your project code

Write your code in: projects/workload-operations/my_model.py (copy projects/workload-operations/starter/model.py first: cp projects/workload-operations/starter/model.py projects/workload-operations/my_model.py).

Verify your implementation: python3 projects/workload-operations/tests/check.py --stage all --impl projects/workload-operations/my_model.py

Run the staged project checks on your implementation in projects/workload-operations/my_model.py and record your transfer experiment results in your portfolio.

Open An observable, restartable local workload

Open Ship a tracked and containerized model release

What still needs further work

Authenticated review, multi-writer promotion, staged cloud rollout, SLO alerting and live-model abuse evaluation still require separate infrastructure and application-specific labs.