Scaling Pinterest ML Infrastructure with Ray: From Training to End-to-End ML Pipelines

Summary

The article outlines how Pinterest has expanded the capabilities of Ray beyond traditional training and inference tasks to create a comprehensive machine learning infrastructure. It details the challenges faced with existing Spark-based workflows, such as slow data pipelines and inefficient feature iterations, and describes how Ray's integration has led to significant improvements in feature development, sampling, and labeling processes. Key innovations include the development of a Ray Data native pipeline API, efficient data joining with Iceberg bucket joins, and optimizations for large-scale workloads, resulting in a tenfold reduction in ML iteration times and lower infrastructure costs.

Key Learnings

• 1Ray can be effectively utilized to streamline the entire ML infrastructure, not just training and inference.
• 2Implementing a Ray Data native pipeline API allows for on-the-fly feature transformation, significantly reducing preprocessing time.
• 3Iceberg bucket joins enable efficient feature joining across datasets without the need for extensive precomputation.
• 4Optimizing Ray's data processing capabilities can lead to substantial performance improvements in ML workflows.
• 5The integration of caching mechanisms can enhance iteration speeds and reduce redundant computations in ML experiments.

Who Should Read This

Senior Machine Learning Engineers implementing scalable ML infrastructures using Ray and seeking to optimize data processing workflows.

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