Course
Fully Automated MLOps
IntermediateSkill Level
Updated 11/2024TheoryMachine Learning4 hr15 videos53 Exercises3,700 XP5,724Statement of Accomplishment
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Discover Full Automation in MLOps
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.Learn About MLOps Architecture
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.Explore CI/CD/CM/CT MLOps Techniques
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.Understand Automation in Deployment Strategies
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.Start learning, gain knowledge in this highly in-demand field, and discover how to apply automation when designing MLOps systems.
Prerequisites
MLOps Deployment and Life Cycling1
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.
2
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.
3
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.
4
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.
Fully Automated MLOps
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FAQs
Is this a hands-on coding course or a conceptual course?
It is primarily a conceptual course using Theory as its technology. You will engage in hands-on exercises to explore MLOps architecture components, but the focus is on understanding automation principles.
What does CI/CD mean in the context of MLOps and does this course cover it?
CI/CD stands for Continuous Integration and Continuous Delivery. This course teaches CI/CD alongside Continuous Monitoring and Continuous Training to automate the full ML lifecycle.
What is hidden technical debt in machine learning and is it addressed?
Hidden technical debt refers to maintenance costs that accumulate in ML systems over time. The course explains its impact and how MLOps automation helps avoid it in deployments.
What prerequisites should I complete before this course?
You need MLOps Concepts, MLOps Deployment and Life Cycling, Understanding Data Engineering, and Understanding Machine Learning to be prepared for this course.
How long does this course take to complete?
It has 4 chapters and 53 exercises. Despite the 240-minute estimate, most learners finish in about 1.6 hours since it is conceptual rather than coding-heavy.
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