You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

17. Other Books You May Enjoy

What is good data for ML?

Good ML models are a result of training on good-quality data. Before proceeding to ML training, a pre-requisite is to have good-quality data. Therefore, we need to process the data to increase its quality. So, determining the quality of data is essential. Five characteristics will enable us to discern the quality of data, as follows:

Accuracy: Accuracy is a crucial characteristic of data quality, as having inaccurate data can lead to poor ML model performance and consequences in real life. To check the accuracy of the data, confirm whether the information represents a real-life situation or not.
Completeness: In most cases, incomplete information is unusable and can lead to incorrect outcomes if an ML model is trained on it. It is vital to check the comprehensiveness of the data.
Reliability: Contradictions or duplications in data can lead to the unreliability of the data. Reliability is a vital characteristic; trusting the data is essential...

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

What is good data for ML?

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

What is good data for ML?

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