Understanding ML engineering

ML engineering is the process of applying software engineering principles and practices to ML projects. In the context of this book, ML engineering is also a discipline that facilitates applying application development practices to the data science lifecycle. When you write a traditional application such as a website or a banking system, there are processes and tools to assist you in writing high-quality code right from the start. Smart IDEs, standard environments, continuous integration, automated testing, and static code analysis are just a few examples. Automation and continuous deployment practices enable organizations to deploy applications many times in a day and with no downtime.

ML engineering is a loose term that brings the benefits of traditional software engineering practices to the model development world. However, most data scientists are not developers. They may not be familiar with software engineering practices. Also, the tools that...