This module establishes the foundation for understanding how Java manages memory in ML applications and why memory optimization is critical for performance. Learners will explore JVM architecture (heap, stack, metaspace), identify memory-intensive patterns common in ML pipelines (feature transformations, tensor manipulation, data preprocessing), and understand how garbage collection cycles impact model inference latency. Through profiling tool setup and hands-on exercises with real ML workloads, students will learn to capture and interpret basic memory metrics, recognize common bottlenecks like excessive object creation and large collection overhead, and prepare their development environment for systematic memory analysis.

This module applies comprehensive optimization techniques to build production-ready, memory-efficient ML systems. Learners will implement strategies to reduce object overhead in data pipelines through buffer pooling and primitive collections (Trove, FastUtil), tune JVM parameters for ML inference workloads including heap sizing and GC algorithm selection (G1GC, ZGC, Shenandoah), and optimize for containerized environments (Docker, Kubernetes). The capstone project guides students through an end-to-end optimization of a real ML service—from baseline profiling through data structure fixes and GC tuning to final validation—achieving measurable improvements in throughput (20-40%), latency reduction, and memory footprint while demonstrating production monitoring best practices.