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 classification

Classification, or predicting the category of something based on its attributes, is one of the key techniques of machine learning. Just like regression, you first need to prep your data before training it with AutoML. In this section, you will first navigate to your Jupyter notebook, load in your data, and transform it for use with AutoML.

Just as you loaded in your Diabetes Sample dataset via Jupyter notebooks for regression, you will do the same with the Titanic Training Data dataset. However, this time around you will do much more extensive data transformation before training your AutoML model. This is to build upon your learning; classification datasets do not necessarily require more transformation than their regression counterparts. Identical to the previous chapter, you will begin by opening up a Jupyter notebook from your compute instance.

Navigating to your Jupyter environment

Similar to Chapter 4, Building an AutoML Regression...