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

Chapter 6: Building an AutoML Forecasting Solution

Having built an AutoML regression and classification solution, you are now ready to tackle a more complicated problem: forecasting. Forecasting is inherently a much more complex technique than either classification or regression. Those two machine learning (ML) problem types assume that time is irrelevant. Regardless of how much time passes, your diabetes model will always be able to accurately predict whose condition worsens over time. Your Titanic model will always be able to predict who lives and who dies. In contrast, with forecasting problems, you are always trying to predict future events based on past events; time will always be a factor in your model.

You will begin this chapter similarly to how you began Chapter 4, Building an AutoML Regression Solution, and Chapter 5, Building an AutoML Classification Solution. First, you will navigate to your Jupyter environment, load in data, train a model, and evaluate the results....