Modern deep learning frameworks provide an abstraction layer that separates model design from hardware execution. When you define a neural network in Python, you are describing mathematical intent rather than explicit machine instructions. For instance, a linear layer operation is represented mathematically as:

$y = Wx + b$

To execute this efficiently on a GPU or specialized accelerator, the high-level Python code must be translated into optimized binary kernels. This translation is the primary function of the machine learning compilation stack.

This chapter examines the architecture enabling this translation. Unlike standard compilers that optimize for general-purpose scalar logic, ML compilers focus on tensor algebra and massive parallelism. We will identify why direct execution via interpreters is often insufficient for production workloads and how specific compilation stages address performance bottlenecks.

You will work through the following core topics:

• The Framework-Hardware Gap: Analyzing the overhead introduced by dynamic dispatch and memory management in interpreted languages.
• Compiler Anatomy: Identifying the role of the frontend, optimizer, and backend code generator.
• Compilation Strategies: Comparing Ahead-of-Time (AOT) and Just-in-Time (JIT) approaches to model deployment.
• Graph Capture: Understanding how tools trace execution to build a static computation graph.

By the end of this chapter, you will understand the lifecycle of an ML operator from a line of Python code to a hardware instruction and have a configured environment ready for inspecting compiler internals.