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

Fine-tuning your AutoML forecasting model

In this section, you will first review tips and tricks for improving your AutoML forecasting models and then review the algorithms used by AutoML for forecasting.

Improving AutoML forecasting models

Forecasting is very easy to get wrong. It's easy to produce a model that seems to work in development, but fails to make accurate predictions once deployed to production. Many data scientists, even experienced ones, make mistakes. While AutoML will help you avoid some of the common mistakes, there are others that require you to exercise caution. In order to sidestep these pitfalls and make the best models possible, follow these tips and tricks:

  • Any feature column that you train with has to be available in the future when you make a prediction. With OJ Sales Sample, this means that, if you want to predict the quantity of sales 6 weeks out and include price as an input variable, you need to know the price of each product 6 weeks...