Machine Learning Infrastructure and Best Practices for Software Engineers
Machine Learning Infrastructure and Best Practices for Software Engineers
Take your machine learning software from a prototype to a fully fledged software system
About the Book
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.
About the Author
Table of Contents
- Machine Learning Compared to Traditional Software
- Elements of a Machine Learning Software System
- Data in Software Systems – Text, Images, Code, Features
- Data Acquisition, Data Quality and Noise
- Quantifying and Improving Data Properties
- Types of Data in ML Systems
- Feature Engineering for Numerical and Image Data
- Feature Engineering for Natural Language Data
- Types of Machine Learning Systems – Feature-Based and Raw Data Based (Deep Learning)
- Training and evaluation of classical ML systems and neural networks
- Training and evaluation of advanced algorithms – deep learning, autoencoders, GPT-3
- Designing machine learning pipelines (MLOps) and their testing
- Designing and implementation of large scale, robust ML software – a comprehensive example
- Ethics in data acquisition and management
- Ethics in machine learning systems
- Integration of ML systems in ecosystems
- Summary and where to go next
About the Publisher
This book is published on Leanpub by Packt Publishing Ltd
Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com
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