Scaling Training and Batch Inference - A Deep Dive into Ray AIR's Data Processing Engine

Scaling Training and Batch Inference - A Deep Dive into Ray AIR's Data Processing Engine

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Overview

2 of 17

2 of 17

Overview

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Scaling Training and Batch Inference - A Deep Dive into Ray AIR's Data Processing Engine

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  1. 1 Intro
  2. 2 Overview
  3. 3 ML Pipelines Must Scale with Data
  4. 4 Distributed Data-Parallel to the Rescue
  5. 5 Scaling the Typical ML Pipeline
  6. 6 Possible Solution - Coordinated Pipelining
  7. 7 Ray Datasets: AIR's Data Processing Engine
  8. 8 Avoiding GPU Data Prep Stalls
  9. 9 Dataset Sharding
  10. 10 Parallel I/O and Transformations
  11. 11 Dataplane Optimizations
  12. 12 Pipelining Ingest with Training
  13. 13 Pipelining Ingest with Inference
  14. 14 Autoscaling Actor Pool for Inference
  15. 15 Per-epoch Shuffling - Distributed
  16. 16 ML engineer at Telematics Startup
  17. 17 Summary

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