التسجيل على دفعات وفق جدول زمني

Batch scoring runs your model over a whole dataset at once, on a schedule, and saves every prediction for later use. 📦

In batch mode nobody waits for a live answer. The job runs offline, so a few minutes or hours of runtime is perfectly fine.

Think of scoring churn risk for every customer each night. One job reads the table, predicts for all rows, and writes the results back.

Every batch job follows the same shape: load the input data, call predict on it, then store the output where consumers can read it.

Models love arrays. Passing a whole DataFrame to predict is far faster than looping one row at a time, because work is vectorized.

Batch predictions land in a table, file, or warehouse. Apps then just look up the stored score instead of calling the model live.

A scheduler like cron or Airflow fires the job at a fixed time. This cadence can be hourly, nightly, or weekly to fit how fast data changes.

You spin up compute, score everything, then shut it down. This burst pattern means you pay only for the minutes the job actually runs.

The cost of batch is staleness. A score from last night may be hours old, so batch fits cases where slightly stale answers are acceptable.

Design the job so re-running it gives the same result. An idempotent job can be retried after a failure without creating duplicates.

Choose batch when predictions cover known entities, freshness in hours is fine, and you want simplicity and low cost over instant answers.

What is the main downside of batch scoring?

Batch scoring predicts over a full dataset on a schedule, stores results for fast lookup, and trades freshness for low cost and simplicity.