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


Automating ML solutions in an end-to-end fashion is no easy task and if you've made it this far, feel proud. Most modern data science organizations can easily train models. Very few can implement reliable, automated, end-to-end solutions as you have done in this chapter.

You should now feel confident in your ability to design end-to-end AutoML solutions. You can train models with AutoML and create ML pipelines to score data and retrain models. You can easily ingest data into Azure and transfer it out of Azure with ADF. Furthermore, you can tie everything together and create ADF pipelines that seamlessly ingest data, score data, train data, and push results to wherever you'd like. You can now create end-to-end ML solutions.

Chapter 11, Implementing a Real-Time Scoring Solution, will cement your ML knowledge by teaching you how to score data in real time using Azure Kubernetes Service within AMLS. Adding real-time scoring to your batch-scoring skillset will make...