Developing Machine Learning Models for Production

Course Description

Much of today’s machine learning-related content focuses on model training and parameter tuning, but 90% of experimental models never make it to production, mainly because they were not built to last. In this course, you will see how shifting your mindset from a machine learning engineering mindset to an MLOps (Machine Learning Operations) mindset will allow you to train, document, maintain, and scale your models to their fullest potential.

Experimenting with ML models is often enjoyable but can be time-consuming. Here, you will learn how to design reproducible experiments to expedite this process while writing documentation for yourself and your teammates, making future work on the pipeline a breeze.

You will learn best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible.

By considering model and data complexity and continuous automation, you can ensure that your models will be scaled for production use and can be monitored and deployed in the blink of an eye.

What You’ll Learn

Moving from Research to 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.

ML in Production Environments

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.

Ensuring Reproducibility

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.

Testing ML Pipelines

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.