Chapter00 Introduction

If you find this content useful, consider buying this book:

  • Take the duke/coursera specialization

    • Introduction #

    Welcome the journey to mastering cloud computing for data. This book is designed to be extremely practical and should help you get your job done no matter which chapter you open up. The material derives from both real-world projects I have done in my career and teaching this material at top universities around the world.

    About the Cover #

    The cover is the top of the Haleakala volcano in Maui. The hike can be extremely challenging because of extreme weather, high altitude, and intense UV rays and wind. A four walk can feel like running a couple of marathons back to back. This walk is an excellent mindset for cloud computing, be prepared for anything, and you will get rewarded with an epic adventure.

    What you will learn #

    So what will you learn in this book? Here are a few examples of what you should be able to accomplish after reading the book:

    • Create Cloud Machine Learning workspaces
    • Manage Machine Learning experiments and compute contexts
    • Create models by using Machine Learning Designer GUI tools
    • Create a training pipeline with various cloud platforms
    • Automate and monitor a pipeline
    • Automate and watch an experiments
    • Use AutoML
    • Deploy machine learning model to Azure, GCP, and AWS
    • Load test the machine learning endpoints
    • Debug production issues with the machine learning models
    • Build useful logging for the machine learning model
    • Build useful APIs for the machine learning model
    • Determine the appropriate cloud architecture for production deployment (i.e., GPU, elastic endpoints, etc
    • Apply continuous delivery to the machine learning system
    • Build a Cloud-based “Machine Learning engineering” portfolio project.
    • Create a Machine Learning engineering solution to a business problem
    • Use originality to create compelling screencasts of their projects