Fully Automated MLOps

Course Description

MLOps is the set of practices developed to help you deploy and maintain machine learning models in production. Nowadays, in industry and research, MLOps is in the spotlight as a way to ensure that ML systems produce value.

In this course, you will learn how to use automation in MLOps to deploy ML systems that can deliver value over time. You’ll learn how hidden technical debt affects ML systems and the value they produce. You’ll also understand how automating and streamlining the stages of the ML lifecycle can help the operation and scaling of ML systems.

You will use hands-on and interactive exercises to learn about the components of an MLOps architecture and how these are necessary to enable the full automation of ML systems.

As you progress, you’ll learn how automated CI/CD, together with Continuous Monitoring (CM) and Continuous Training (CT), are key techniques to avoid technical debt in your ML deployments.

By the end of the course, you’ll understand how automation with MLOps can improve how you deploy your ML systems to the real world, providing your deployments with robustness and scalability.

What You’ll Learn

Introduction: to Fully Automated MLOps

In this first chapter, we motivate the use of MLOps in an industrial setting. You’ll learn about its importance in supporting the generation of value in businesses. You’ll also recap the ML stages, focusing on how MLOps enhances these. At the end of the chapter, you’ll explore a reference architecture for a fully automated MLOps system. You will then use this architecture to explore components important for any MLOps system and a starting point for the rest of the course.

Automation Patterns

In this chapter, you’ll dive into the exciting world of automation and learn how to design more resilient and efficient ML systems. You’ll start by understanding the fundamentals of automation in MLOps systems and then move on to discover the power of design patterns and fail-safe mechanisms. You’ll also learn how to implement automated testing in MLOps systems and how to use hyperparameter tuning to optimize your models and workflows. By the end of this chapter, you’ll be equipped with the skills and knowledge necessary to build and manage fully automated MLOps systems that are both efficient and reliable.

Fully Automated MLOps Architecture

In this chapter, you will continue your exploration of the critical components that make up a fully automated MLOps system. First, you’ll examine the importance of orchestration in MLOps and how it helps to ensure the efficiency and scalability of ML pipelines. After this, you’ll examine the different deployment strategies in MLOps and learn how to choose the right strategy for your system. Finally, you’ll learn about CI/CD/CT/CM and how it complements orchestration and best practices to achieve full automation in MLOps systems. With these lessons under your belt, you will be better equipped to build a fully automated MLOps system that is efficient, accurate, and reliable.

Automation in MLOps Deployments

In this final chapter, you’ll delve into the crucial components of an automated MLOps architecture. From understanding automated experiment tracking and the model registry to exploring the feature store and the role of the metadata store, this chapter is designed to equip you with a comprehensive understanding of the intricacies of a fully automated MLOps system. Whether you’re a seasoned ML practitioner or just starting out, this chapter will provide you with the knowledge and skills necessary to design automated MLOps workflows.