Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
Table of Contents (17 chapters)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions


Advanced solutions like the MMSA are at the bleeding edge of ML and AI. It is a truly state-of-the-art technology and now it's another tool in your belt.

You've not only run all three notebooks on the OJ Sales data, but you have also converted the code to take in other datasets and understand how it works. Prepping data, training models, and forecasting the future using the MMSA are all things you have done and could do again. You may already have a use case to which you can apply it, or you may have to wait a few more years until your company is ready, but you are prepared.

Chapter 8, Choosing Real-Time versus Batch Scoring, continues your journey at the forefront of the ML world. Once you build a model in AutoML, the next step is to deploy it, and there are two options: batch versus real-time scoring. You will learn when to use batch scoring, when to use real-time scoring, and the main differences between the two. Mastering these concepts is key to successfully...