Scaling Model Evaluation at Pinterest with Ray (Part 4)

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Scaling Model Evaluation at Pinterest with Ray (Part 4)

Data infrastructure Medium

Pinterest optimized its evaluation platform by computing metrics incrementally within Ray inference jobs, eliminating the need to read predictions back from S3.

What: The platform now aggregates partial evaluation metrics in-memory during inference rather than exporting raw predictions to storage. This shift reduced end-to-end evaluation time for feature-importance tasks by up to 32%.

Why it matters: IO-bound processes, like saving large prediction datasets to object storage only to read them back for metric calculation, remain major bottlenecks in large-scale machine learning workflows.

Original article

Pinterest used to run inference with Ray, write predictions to S3, then start a Spark job to read them back and compute metrics. Its new evaluation platform computes partial metrics batch by batch inside the Ray inference job and combines them at the end. That cut end-to-end evaluation time by up to 32% for feature-importance runs.

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