Machine Learning Infrastructure and Best Practices for Software Engineers
- Paperback: 346 pages
- Publisher: WOW! eBook (January 31, 2024)
- Language: English
- ISBN-10: 1837634068
- ISBN-13: 978-1837634064
Machine Learning Infrastructure and Best Practices for Software Engineers: Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products
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.
- Identify what the machine learning software best suits your needs
- Work with scalable machine learning pipelines
- Scale up pipelines from prototypes to fully fledged software
- Choose suitable data sources and processing methods for your product
- Differentiate raw data from complex processing, noting their advantages
- Track and mitigate important ethical risks in machine learning software
- Work with testing and validation for machine learning systems
Towards the end of the Machine Learning Infrastructure and Best Practices for Software Engineers book, 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.