Overview

Memory inefficiencies cause 40% of Java ML application performance problems, making optimization critical for production systems. This course equips Java developers to build memory-efficient ML systems through hands-on profiling with Java Flight Recorder and systematic optimization of collections and JVM settings. You'll diagnose bottlenecks using heap analysis, optimize pipelines by replacing inefficient structures like LinkedList with ArrayDeque, and tune garbage collectors for low-latency inference. This course eliminates memory bottlenecks, degrading ML production systems. With hands-on labs, you will simulate production scenarios, including GC pause analysis and container optimization. This course is for Java developers, ML engineers, and backend professionals looking to boost performance, reduce latency, and optimize memory in production ML systems. Learners should know Java, JVM basics, and collections, with command-line skills and familiarity with ML pipelines and build tools like Maven or Gradle. By course completion, you'll identify allocation hotspots, reduce GC overhead by 30%+, configure JVM for sub-100ms latency, and deploy optimized containerized ML services.

Syllabus

Java Memory Model for ML Workloads

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.

Profiling and Analyzing Memory Usage

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.

Practical Optimization Strategies for ML Applications

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.