MLOps Concepts

Course Description

Learn about Machine Learning Operations (MLOps)

Understanding MLOps concepts is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production.

In this course, you’ll learn what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. After learning about the essential MLOps concepts, you’ll be well-equipped in your journey to implement machine learning continuously, reliably, and efficiently.

Discover How Machine Learning Can be Scaled and Automated

How can we scale our machine learning projects using the minimum time and resources? And how can we automate our processes to reduce the need for manual intervention and improve model performance? These are fundamental Machine Learning questions that MLOps provides the answers to.

In this MLOps course, you’ll start by exploring the basics of MLOps, looking at the core features and associated roles. Next, you’ll explore the various phases of the machine learning lifecycle in more detail.

As you progress, you’ll also learn about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. You’ll explore key MLOps concepts, giving you a firmer understanding of their applications.

What You’ll Learn

Introduction to MLOps

First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.

Deploying Machine Learning into Production

In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.

Design and Development

Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.

Maintaining Machine Learning in Production

Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.