Explain batch inference design
Company: Anthropic
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
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
Quick Answer: 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.