Building a machine learning model is one part of the process. Making it perform reliably at scale is a different and more complex engineering challenge. The effectiveness of any advanced AI system is fundamentally constrained by the design of its underlying infrastructure. Seemingly small architectural choices in compute, networking, and storage can lead to significant differences in training time, inference latency, and operational cost.

This chapter establishes the foundational hardware and software patterns for high-performance AI platforms. We will cover the specific infrastructure components required to support demanding machine learning workloads.

You will learn to:

• Apply MLOps principles to large, production-oriented environments.
• Analyze the trade-offs between CPU, GPU, and TPU architectures for specific ML tasks.
• Understand the role of high-bandwidth interconnects like NVLink and InfiniBand in multi-node training.
• Compare different storage solutions, from object storage to parallel file systems, for ML data access patterns.
• Evaluate network topologies to prevent communication bottlenecks in distributed clusters.

We will conclude with a hands-on practical to configure your local and cloud environments with the necessary tooling for the course. This setup will prepare you for the technical implementations in the chapters that follow.