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MLOps Deployment and Life Cycling

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

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4 Hours16 Videos54 Exercises

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Course Description

MLOps Deployment and LifeCycling

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.

Learn About the MLOps Lifecycle

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. You’ll start by exploring the MLOps lifecycle, discovering the importance of MLOps and the key functional components for model development, deployment, monitoring, and maintenance.

Develop ML Code for Deployment

Next, you’ll learn how to develop models for deployment and how to write effective ML code, leverage tools, and train ML pipelines. As you progress, you’ll cover how to deploy your models, exploring different deployment environments and when to use them. You’ll also develop strategies for replacing existing production models and examine APIs.

Learn How to Monitor Your Models

As you complete the course, you’ll discover the crucial performance metrics behind monitoring and maintaining your ML models. You’ll learn about drift monitoring in production, as well as model feedback, updates, and governance. By the time you’re finished, you’ll understand how you can use MLOps lifecycle to deploy your own models in production.
  1. 1

    MLOps in a Nutshell


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

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    The modern MLOps framework
    50 xp
    ML workflows
    100 xp
    MLOps benefits
    50 xp
    Life-cycling stages
    50 xp
    App vs. model
    100 xp
    50 xp
    The model life cycle: recap
    100 xp
    MLOps components
    50 xp
    Automated sequence
    50 xp
    Stores and registries
    100 xp
    DevOps or MLOps?
    100 xp
  2. 2

    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.

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  3. 3

    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.

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  4. 4

    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.

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In the following tracks

MLOps Fundamentals


George Boorman
Arne Warnke
James Chapman


MLOps Concepts
Nemanja Radojković HeadshotNemanja Radojković

Senior Data Scientist @ Euroclear

Nemanja's areas of expertise include Machine Learning, Text Mining and Computer Vision. He is currently a Senior Data Scientist at Euroclear, where he helps businesses to harvest the power of their data. He has worked with industries as varied as manufacturing, life sciences & healthcare, and transportation services & infrastructure.
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