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

Creating an ML pipeline

ML pipelines are Azure's solution for batch scoring ML models. You can use ML pipelines to score any model you train, including your own custom models as well as AutoML-generated models. They can only be created via code using the Azure ML Python SDK. In this section, you will code a simple pipeline to score diabetes data using the Diabetes-AllData-Regression-AutoML model you built in Chapter 4, Building an AutoML Regression Solution.

As in other chapters, you will begin by opening your compute instance and navigating to your Jupyter notebook environment. You will then create and name a new notebook. Once your notebook is created, you will build, configure, and run an ML pipeline step by step. After confirming your pipeline has run successfully, you will then publish your ML pipeline to a pipeline endpoint. Pipeline endpoints are simply URLs, web addresses that call ML pipeline runs.

The following steps deviate greatly from previous chapters. You...