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

Obtaining better AutoML performance

Congratulations! You have built your first model and it performs very well. However, there are a lot of little things you can do to improve performance. You will build many more models in the future, after all, and in order to build the best models, you need to know all of the tips and tricks. Here's a list of tips and tricks to end this chapter:

  • Additional feature engineering will often provide superior results. Feature engineering just means transforming data in ways that make it easier for machine learning algorithms to find patterns. Binning ticket prices and age into buckets in the Titanic data, for example, may provide you with superior results compared to just using prices and age as numeric columns.
  • Speaking of binning, you can always bin a regression problem to turn it into a classification problem. If you're trying to predict the average lifespan of a human being, for example, you can try to predict a range of numbers...