Discover MLOps for BusinessLearn the essential concepts and practices of MLOps, an emerging set of tools and techniques for automating and scaling machine learning applications. Machine learning model development used to be a lengthy manual task, and most models never made it into production. With MLOps, businesses can effectively scale and automate the design, development, and operation of machine learning models.
Learn to Use Machine Learning Operations in Your BusinessThis course will teach you what MLOps is and how you can use it to become a fully mature machine-learning company. You will learn about the requirements for MLOps, the tools, techniques, and people involved, and how to avoid common pitfalls. You’ll start by learning about the main elements of MLOps and why it is critical for businesses that want to design, develop, and operate multiple machine learning applications.
Explore the MLOps Life CycleNext, you’ll explore the entire MLOps life cycle, from design to development, deployment, and operations. In Chapter 3, you’ll learn about the main challenges and risks of deploying machine learning models in practice. Finally, you’ll look at the best practices and case studies for successfully implementing MLOps in the real world. By the end of the course, you'll have a deep understanding of how to design, develop, and operate machine learning applications at scale and will be able to leverage the impact of machine learning on your business.
The first chapter will introduce MLOps and why it is necessary for businesses that want to design, develop, and operate multiple machine learning applications simultaneously. You will learn about the main elements of MLOps, such as scaling and automation, its benefits, and why MLOps remain challenging. You will also explore what it takes to start the MLOps journey both from a technological and managerial perspective.What is MLOps?50 xpMLOps: a set of practices to ...50 xpWhat are characteristics of MLOps?100 xpThe business arguments for MLOps50 xpRelevant MLOps fields50 xpHow to invest in MLOps50 xpReasons to invest in MLOps100 xpLaying the foundation for MLOps50 xpMLOps team members50 xpPrerequisites of MLOps100 xpElements of MLOps100 xp
The MLOps Life Cycle
In the second chapter, you’ll learn about the entire MLOps life cycle from design to development, deployment, and operations. You’ll explore why monitoring is essential for productive machine learning applications and why we must regularly re-train machine learning models.MLOps explained end-to-end50 xpTen important MLOps life cycle steps50 xpThe main stages of an MLOps application100 xpImportant tasks in the main MLOps stages100 xpThe design phase50 xpPostponing design phase tasks50 xpDesign phase for a future ML supply chain app100 xpThe development phase50 xpDevelopment tasks100 xpRoles in the development phase100 xpMonitoring, re-training and replacing MLOps applications50 xpCharacteristics of the MLOps life cycle50 xpIndividual responsibilities in developing an MLOps use case100 xpThe MLOps life cycle100 xp
MLOps: From Theory to Practice
In the third chapter, you will move from theory to practice and discover the main challenges and risks of deploying machine learning models. You’ll also learn how MLOps teams successfully operate and what management can do to foster successful scaling machine learning.Business challenges and risks related to MLOps50 xpWhat makes MLOps challenging?50 xpTraditional software vs. MLOps100 xpPotential MLOps business risks100 xpHow MLOps teams successfully operate50 xpDevOps-style teams50 xpChallenges to implement MLOps100 xpThe state of MLOps today50 xpA false statement about MLOps50 xpReasons that MLOps is challenging100 xp
MLOps in the wild
The final chapter will demonstrate how to successfully jumpstart your business's MLOps journey by discussing best practices and pitfalls to avoid. Finally, you’ll examine the different levels of MLOps maturity and conclude the course with a real-life case study about designing, developing, and operating a machine learning application for critical production processes.How businesses can become fully MLOps ready50 xpValid MLOps KPIs50 xpOrder deployment maturity100 xpMLOps maturity100 xpMLOps best practices and pitfalls50 xpWhat do we need to test?50 xp(Not) A best practice100 xpMLOps case study: Increasing profits with MLOps50 xpKey points of the case study100 xpWrap-up50 xp
PrerequisitesMachine Learning for Business
Arne WarnkeSee More
Head of Emerging Curriculum at DataCamp
Arne is responsible for machine learning engineering and data engineering content at DataCamp. He is a mathematician with a Ph.D. in economics and applied statistics. Before joining DataCamp, Arne worked for several years as a data scientist.