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Quality evaluation and model upgrades

Quality evaluation and model upgrades

articleSelf-paced

Ship model changes with the discipline of software releases.

1. Task datasets

Use representative documents with expected labels and difficult negatives. Keep a held-out set and report per-class failures so a frequent class does not dominate the score.

2. Artifact compatibility

Test tokenizer, prompt format and runtime compatibility for each model version. A new artifact may load successfully yet interpret the same prompt differently.

3. Staged activation

Gate activation on integrity and smoke tests, then monitor quality and crash signals. Keep rollback possible without forcing users to redownload the entire app.

Worked scenario

An updated tokenizer changes classification behavior while the runtime still loads. A fixed regression set catches the behavioral change.

Apply it

Write a model release checklist and simulate reverting to the previous verified artifact.

Check your understanding

You can roll back a model independently from ordinary user documents. Explain the decision and show evidence from your implementation or design. If you cannot demonstrate it yet, revisit the relevant section before continuing.

YOUR LEARNING JOURNEY

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Quality evaluation and model upgrades | On-device AI on Android | Android Engineers