With your infrastructure provisioned and your applications containerized, the next step is to ensure your machine learning workloads run efficiently. Having access to powerful hardware like GPUs does not guarantee optimal performance. Inefficient code or poorly structured data pipelines can lead to significant resource underutilization, increasing both training time and operational costs. The goal is to maximize throughput, reduce training duration, and lower inference latency.

This chapter provides a set of practical techniques to improve the performance of your AI systems. We will begin by using profiling tools to pinpoint performance bottlenecks, whether they lie in CPU processing, GPU computation, or data I/O. From there, you will learn specific optimization strategies. We will cover distributed training methods to scale your training jobs across multiple GPUs. You will also learn to implement mixed-precision training, which uses formats like $FP16$ to accelerate computation and reduce memory usage. For deployment, we will address model quantization to create smaller, faster models for inference. Finally, we will examine how to build efficient data pipelines that keep your compute resources fully utilized.