Part 2 focuses on the application of high-performance computing (HPC) for machine learning. It includes a hands-on implementation of an end-to-end solution starting with analyzing large amounts of data and then covering distributed training and deploying models at scale, including performance optimization and machine learning at the edge.

This part comprises the following chapters:

• Chapter 5, Data Analysis
• Chapter 6, Distributed Training of Machine Learning Models
• Chapter 7, Deploying Machine Learning Models at Scale
• Chapter 8, Optimizing and Managing Machine Learning Models for Edge Deployment
• Chapter 9, Performance Optimization for Real-Time Inference
• Chapter 10, Data Visualization