Model release gates and rollback drill

The problem you will solve

A fictional prediction service gets a new model version. Build a release workflow that rejects regressions, keeps the previous version available and records why promotion or rollback occurred. The focus is operational evidence, not training a state-of-the-art model.

Before you start

Model artifacts, HTTP services, containers and basic test automation.

Suggested tech stack

• Python
• Docker
• FastAPI
• SQLite
• pytest

Data plan

Use a tiny synthetic classification task with a frozen labeled evaluation set. Prepare one good candidate, one deliberately degraded candidate and a shifted input fixture; label all results as synthetic.

Hardware and cost

Train a very small CPU model and run two local service versions. A paid cloud deployment is optional and should have a spending cap and teardown plan. Do not imply a laptop drill proves production reliability.

Define the input and output

Use this illustrative contract as a starting point, then specify required fields, validation and error responses in your own implementation. It is not a complete API schema.

Release record: candidate_version, dataset_hash, quality_result, latency_result, decision, previous_version
Service: /predict returns prediction + model_version; /health reports readiness

Your five build milestones

Finish and test one milestone before extending the system. Keep an experiment log with the decision, the alternative you rejected and the evidence behind the choice.

1. Version the artifact

   Bundle model, preprocessing and input schema under one version. Hash the evaluation dataset and record dependencies so the same candidate can be rebuilt and inspected.

2. Create promotion gates

   Define minimum recall for the important class and a latency budget before testing. Run schema, quality and malformed-input tests; fail closed when results are unavailable.

3. Deploy two versions

   Run current and candidate services locally with distinct version responses. Route a controlled set of synthetic requests to the candidate and retain the current artifact.

4. Detect a regression

   Replay shifted inputs and inject slow responses. Compare prediction outcomes, errors and latency; distinguish input drift alerts from measured accuracy, which requires labels.

5. Run the rollback drill

   Switch routing back to the previous version and verify new requests use it. Save a timestamped event log, rollback duration and a short incident explanation.

How to evaluate your project

Set your acceptance criteria before looking at the final test results. The checks below are proposed experiments, not measured outcomes or a guarantee that a particular model will pass.

• Show that the deliberately degraded candidate cannot pass the quality gate.
• Record success, error and latency distributions for a declared number of replay requests.
• Verify rollback restores the previous model and preprocessing together, and document the limits of synthetic load testing.

Save your test cases, run command, dependency versions and results together. Compare on identical inputs, retain failures in the denominator and report sample sizes. If a change improves one metric but harms another, explain which trade-off fits your use case.

Failure modes worth demonstrating

• Promoting when an evaluation job failed or overwriting the last known-good artifact.
• Calling unlabeled drift a proven accuracy loss, or rolling back the model without its preprocessing.

Optional extension

Add a shadow route that records candidate predictions without serving them to users, then compare its operational footprint.

Turn the build into portfolio evidence

Your repository should let another person understand the problem and reproduce the core evaluation without needing your private credentials.

• A README with problem, intended user, architecture, setup commands and a small input/output example.
• A permitted sample dataset, its origin or generator, a frozen test split and a single evaluation command.
• A results table comparing your baseline with your final approach, including failures and sample counts.
• A short demo or screenshots, a limitations section and one decision you would change with more time.
• Environment-variable names without secret values, a dependency list and attribution/license notices for any reused code or datasets.

Resume framing

Describe this as a personal or educational project. State what you implemented, how you tested it and what your measured results were. Replace placeholders with your own evidence; do not claim employer impact, production traffic or accuracy you did not measure.

Built model release gates and rollback drill using Python, Docker, FastAPI; evaluated against [baseline] on [N held-out cases], documented [measured result] and [important limitation].

Be ready to explain your decisions

• Which failures should block a release?
• How do you monitor quality when labels arrive later?

Answer with a concrete failure from your implementation, the options you considered, your measured evidence and the limitation that remains.

Google documentation and GitHub references

The project scope, milestones and evaluation plans above are original AIJobFever educational material. These official Google resources offer background or implementation examples; they are not finished implementations of this blueprint. No Google endorsement is implied. Review repository licenses, supported platforms and current costs before reusing code or provisioning services.

• Google Cloud: MLOps GitHub example ↗