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?

MLOps: a set of practices to ...

What are characteristics of MLOps?

The business arguments for MLOps

Relevant MLOps fields

How to invest in MLOps

Reasons to invest in MLOps

Laying the foundation for MLOps

MLOps team members

Prerequisites of MLOps

Elements of MLOps

Introducing MLOps

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-end

Ten important MLOps life cycle steps

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Design

(Model) Development

Operations

The main stages of an MLOps application

Development

Important tasks in the main MLOps stages

The design phase

Postponing design phase tasks

Blueprint for Success: Designing Your MLOps Project

The development phase

MLOps Development Phase

Development tasks

Data Engineer

Data Scientist

Software Engineer

Roles in the development phase

Monitoring, re-training and replacing MLOps applications

Characteristics of the MLOps life cycle

Data Scientist / ML Engineer

Individual responsibilities in developing an MLOps use case

MLOPs process

The MLOps life cycle

The MLOps Life Cycle

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 MLOps

What makes MLOps challenging?

Traditional software vs. MLOps

Potential MLOps business risks

How MLOps teams successfully operate

DevOps-style teams

Challenges to implement MLOps

The state of MLOps today

A false statement about MLOps

Reasons that MLOps is challenging

MLOps: From Theory to Practice

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 ready

Valid MLOps KPIs

Order deployment maturity

MLOps maturity

MLOps best practices and pitfalls

What do we need to test?

(Not) A best practice

MLOps case study: Increasing profits with MLOps

Key points of the case study

Wrap-up

MLOps in the wild


Learn 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.

This 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.

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.

<h2>Discover MLOps for Business</h2>
Learn 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.
<h2>Learn to Use Machine Learning Operations in Your Business</h2>
This 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.
<h2>Explore the MLOps Life Cycle</h2>
Next, 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.


Machine Learning for Business

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

MLOps for Business

Control the cost of provisioning large amounts of data.

Ensuring all data pipelines are working as expected.

Assessing why a model turned productive produced different results than expected.

Justifying why deep learning has not been considered.

Ensuring that incident logs are running and trigger automated alarms.

Ensuring that the model works in the mobile app as on the server.

Safeguarding that the machine learning codes adheres to the company-wide code guidelines.

Individual responsibilities in developing an MLOps use case

MLOps for Business

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