Implementing MLOps in the Enterprise

Chapter 10. Implementing MLOps Using Rust

Operational efficiency must be at the core of any technology system. MLOps builds upon DevOps, which builds on the concept of kaizen, the Japanese word for continuous improvement. Without continuous improvement, you wouldn’t have DevOps or, by extension, MLOps.

At the heart of continuously improving operations is a simple question: “Can we improve operational performance—from training and inference to packaging and delivery—by ten times or more?” If the answer is yes, as it will be with many organizations using Python for data science, the next question should be: “Why are we not doing it?”

For decades, organizations had few options other than pure C, C++, or C# and Python for machine learning solutions. C++ may provide more efficiency in terms of performance, but Python is generally easier to learn, implement, and maintain, which is why Python has taken off in data science. The hard choice between the performant but complex C++ and the easy-to-learn but comparatively slow Python ultimately results in many companies choosing Python.

But there’s another way. Rust consistently ranks among the most performant and energy-efficient languages. It’s also among the most loved languages in Stack Overflow’s annual developer survey. Though some Python libraries widely used in data science are written in C and can provide some of the performance benefits of running a compiled language, Rust provides a more direct route to bare metal while using a ...