Summary and Quiz
Explore scalable data processing techniques including distributed transformations, containerized jobs, and zero-ETL options. Understand Feature Store architecture and governance to ensure reproducible pipelines and prevent data leakage. This lesson helps you master efficient and secure data preparation for production ML workflows using Amazon SageMaker.
Summary
This chapter describes architectures and operational patterns for scaling and hardening preprocessing for production ML pipelines. It contrasts single-node limitations with distributed processing, outlines containerized execution surfaces and zero-ETL options, and explains Feature Store design and governance to prevent training-serving skew and support reproducible pipelines.
Distributed data transformation
Single-node tools assume the dataset fits in RAM, which leads to out-of-memory failures, swap thrashing, single-threaded throughput limits, and brittle manual chunking. The tight-coupling antipattern wastes accelerator resources when preprocessing runs ...