Optimize Java Memory for ML Performance

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.