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 regression model

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

Improving AutoML regression models

While AutoML will handle most of the complicated data transformations and feature engineering for you, there are a few tips you can follow to increase the accuracy of your model. Some of these tips are true across all three AutoML tasks – regression, classification, and forecasting – while others are regression-specific. Following them will yield higher-performing models and, more importantly, hone your understanding of machine learning techniques. I have listed a few tips and tricks here for quick reference:

  • Fill in null values before passing them on to AutoML. Alternatively, drop any rows that contain a null value. Just because AutoML will automatically fill your null values does not mean that it will do a great job.