You're reading from Automated Machine Learning with Microsoft Azure Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800565319

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Machine Learning

Authors (2):

Dennis Michael Sawyers

Dennis Sawyers

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: AutoML Explained – Why, What, and How

2. Chapter 1: Introducing AutoML FREE CHAPTER

3. Chapter 2: Getting Started with Azure Machine Learning Service

4. Chapter 3: Training Your First AutoML Model

5. Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide

6. Chapter 4: Building an AutoML Regression Solution

7. Chapter 5: Building an AutoML Classification Solution

8. Chapter 6: Building an AutoML Forecasting Solution

9. Chapter 7: Using the Many Models Solution Accelerator

10. Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

11. Chapter 8: Choosing Real-Time versus Batch Scoring

12. Chapter 9: Implementing a Batch Scoring Solution

13. Chapter 10: Creating End-to-End AutoML Solutions

14. Chapter 11: Implementing a Real-Time Scoring Solution

15. Chapter 12: Realizing Business Value with AutoML

16. Other Books You May Enjoy

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...