Explain batch inference design

Q: Explain batch inference design

This question evaluates a candidate's competence in designing scalable, reliable batch inference pipelines for machine learning, covering model artifact management, feature and input versioning, job scheduling and parallelization, output delivery, and operational concerns such as retries, idempotency, backfills, and monitoring.

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You need to generate predictions for a very large offline dataset, such as all users or all products, once per day using an already trained machine learning model. Explain how you would design a batch inference pipeline.

Your answer should cover:

when batch inference is more appropriate than online inference
how input data and features are prepared and versioned
how model artifacts are stored and loaded
how jobs are scheduled, partitioned, and parallelized
how predictions are written to downstream storage and made available to consumers
how to handle retries, idempotency, backfills, and late-arriving data
what metrics you would monitor for correctness, freshness, throughput, and cost

Explain batch inference design

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