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

Determining batch versus real-time scoring scenarios

When confronted with real business use cases, it is often difficult to distinguish how you should deploy your ML model. Many data scientists make the mistake of implementing a batch solution when a real-time solution is required, while others implement real-time solutions even when a cheaper batch solution would be sufficient.

In the following sections, you will look at different problem scenarios and solutions. Read each of the six scenarios and determine whether you should implement a real-time or batch inferencing solution. First, you will look at every scenario. Then, you will read each answer along with an explanation.

Scenarios for real-time or batch scoring

In this section, you are presented with six scenarios. Read each carefully and decide whether a batch or real-time scoring solution is most appropriate.

Scenario 1 – Demand forecasting

A fast-food company is trying to determine how many bags of frozen...