Developing Machine Learning Models for Production

This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.

In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.

In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.

In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.