You're reading from Automated Machine Learning with Microsoft Azure

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800565319

Pages 340 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Dennis Michael Sawyers

Table of Contents (17) Chapters

Preface

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

2. Chapter 1: Introducing AutoML

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

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Determining batch versus real-time scoring scenarios

When confronted with real business use cases, it is often difficult to distinguish how you should deploy your ML model. Many data scientists make the mistake of implementing a batch solution when a real-time solution is required, while others implement real-time solutions even when a cheaper batch solution would be sufficient.

In the following sections, you will look at different problem scenarios and solutions. Read each of the six scenarios and determine whether you should implement a real-time or batch inferencing solution. First, you will look at every scenario. Then, you will read each answer along with an explanation.

Scenarios for real-time or batch scoring

In this section, you are presented with six scenarios. Read each carefully and decide whether a batch or real-time scoring solution is most appropriate.