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

Prepping data for AutoML forecasting

Forecasting is very different from either classification or regression. ML models for regression or classification predict some output based on some input data. ML models for forecasting, on the other hand, predict a future state based on patterns found in the past. This means that there are key time-related details you need to pay attention to while shaping your data.

For this exercise, you are going to use the OJ Sales Simulated Data Azure Open Dataset for forecasting. Similar to the Diabetes Sample Azure Open Dataset you used for regression, OJ Sales Simulated Data is available simply by having an Azure account. You will use this data to create a model to predict future orange juice sales across different brands and stores.

There is one additional key difference; OJ Sales Simulated Data is a file dataset instead of a tabular dataset. While tabular datasets consist of one file containing columns and rows, file datasets consist of many files...