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If you never store what your model saw and said, you cannot diagnose it later. Prediction logging is the foundation of all analysis. 📝

A useful prediction log captures the input features, the model output, the model version, and a timestamp for every request.

Give each prediction a unique request ID. Later, when the true outcome arrives, you join it back to the original input by that ID.

Always store the features alongside the prediction. A score with no inputs cannot tell you why the model decided what it did.

Tag every log with the model version that produced it. After a rollout you can compare old and new behavior side by side.

Write logs as structured JSON, not free text. Machines can then query, filter, and aggregate your predictions automatically.

Predictions usually flow into a prediction store: a database, warehouse, or data lake built for cheap, queryable history.

Write logs asynchronously so storing a record never slows down the response your user is waiting for.

Logs may hold personal data, so apply privacy rules: hash identifiers, drop sensitive fields, and set a retention window.

When real labels land, join them to the logs by request ID. That join is how you measure live accuracy after the fact.

At massive scale, log a representative sample instead of every request to keep storage and cost under control.

Think about reconnecting a prediction to reality.

Log every prediction with its inputs, output, version, and request ID. That structured history is what every later analysis relies on. ✅