Make a Batch Prediction Job Observable
Instrument a batch scoring job with freshness, volume, schema, artifact, and output checks.
The scheduler task is green, but the business received stale predictions. Observable batch scoring proves freshness, completeness, version, and output sanity. Common trap: checking process success while ignoring prediction usefulness. Step 1 Check input freshness and row count before scoring. Freshness catches stale upstream feeds. Row-count bounds catch missing partitions and accidental full-table explosions before the model runs. Step 2 Validate schema and log feature pipeline plus model artifact versions. Schema protects the model contract. Versions make the output traceable during incidents and reviews. Step 3 Compare output count to input count and summarize score distribution. A successful write can…
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