Machine Learning Infrastructure and Best Practices for Software Engineers: Take your machine learning software from a prototype to a fully fledged software system

by Miroslaw Staron

Overview

Although creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products.
The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality.
Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.

Product Details

ISBN-13:	9781837636945
Publisher:	Packt Publishing
Publication date:	01/31/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	346
File size:	10 MB

About the Author

Miroslaw Staron is a professor of Applied IT at the University of Gothenburg in Sweden with a focus on empirical software engineering, measurement, and machine learning. He is currently editor-in-chief of Information and Software Technology and co-editor of the regular Practitioner's Digest column of IEEE Software. He has authored books on automotive software architectures, software measurement, and action research. He also leads several projects in AI for software engineering and leads an AI and digitalization theme at Software Center. He has written over 200 journal and conference articles.

Table of Contents

Table of Contents

1. Machine Learning Compared to Traditional Software
2. Elements of a Machine Learning Software System
3. Data in Software Systems – Text, Images, Code, Features
4. Data Acquisition, Data Quality and Noise
5. Quantifying and Improving Data Properties
6. Types of Data in ML Systems
7. Feature Engineering for Numerical and Image Data
8. Feature Engineering for Natural Language Data
9. Types of Machine Learning Systems – Feature-Based and Raw Data Based (Deep Learning)
10. Training and evaluation of classical ML systems and neural networks
11. Training and evaluation of advanced algorithms – deep learning, autoencoders, GPT-3
12. Designing machine learning pipelines (MLOps) and their testing
13. Designing and implementation of large scale, robust ML software – a comprehensive example
14. Ethics in data acquisition and management
15. Ethics in machine learning systems
16. Integration of ML systems in ecosystems
17. Summary and where to go next