MLOps Deployment and Life Cycling

Overview

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

Explore the modern MLOps framework, including the lifecycle and deployment of machine learning models. In this course, you’ll learn to write ML code that minimizes technical debt, discover the tools you’ll need to deploy and monitor your models, and examine the different types of environments and analytics you’ll encounter.

After you’ve collected, prepared, and labeled your data, run numerous experiments on different models, and proven your concept with a champion model, it’s time for the next steps. Build. Deploy. Monitor. Maintain. That is the life cycle of your model once it's destined for production. That is the Ops part of MLOps.

This course will show you how to navigate the second chapter of your model's journey to value delivery, setting the benchmark for many more to come.

Syllabus

MLOps in a Nutshell

This chapter gives a high-level overview of MLOps principles and framework components important for deployment and life cycling.

Develop for Deployment

This chapter is dedicated to all the considerations we need to make already in the development phase, in order to ensure a smooth ride when we reach the operations.

Our ultimate goal is to explain how to train the model using MLOps best practices and build a model package that enables smooth deployment, reproducibility and post-deployment monitoring.

Deploy and Run

This chapter deals with critical model operations questions such as:
- What are the different ways in which we can serve our models?
- What is an API, and what are its key functionalities?
- How do we thoroughly test our service before making it available to the end users?
- How do we update models in production without service disturbance?

You will learn about batch prediction, real-time prediction, input and output data validation, unit testing, integration testing, canary deployment, and much more.

Monitor and Maintain

This final chapter is dedicated to monitoring and maintaining ML services after they are deployed, as well as to model governance.

You will cover crucial concepts such as verification latency, covariate shift, concept drift, human-in-the-loop systems, and more.