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Machine Learning Infrastructure and Best Practices for Software Engineers

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

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837634064
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Miroslaw Staron Miroslaw Staron
Author Profile Icon Miroslaw Staron
Miroslaw Staron
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1:Machine Learning Landscape in Software Engineering
2. Machine Learning Compared to Traditional Software FREE CHAPTER 3. Elements of a Machine Learning System 4. Data in Software Systems – Text, Images, Code, and Their Annotations 5. Data Acquisition, Data Quality, and Noise 6. Quantifying and Improving Data Properties 7. Part 2: Data Acquisition and Management
8. Processing Data in Machine Learning Systems 9. Feature Engineering for Numerical and Image Data 10. Feature Engineering for Natural Language Data 11. Part 3: Design and Development of ML Systems
12. Types of Machine Learning Systems – Feature-Based and Raw Data-Based (Deep Learning) 13. Training and Evaluating Classical Machine Learning Systems and Neural Networks 14. Training and Evaluation of Advanced ML Algorithms – GPT and Autoencoders 15. Designing Machine Learning Pipelines (MLOps) and Their Testing 16. Designing and Implementing Large-Scale, Robust ML Software 17. Part 4: Ethical Aspects of Data Management and ML System Development
18. Ethics in Data Acquisition and Management 19. Ethics in Machine Learning Systems 20. Integrating ML Systems in Ecosystems 21. Summary and Where to Go Next 22. Index 23. Other Books You May Enjoy

Natural language data in software engineering and the rise of GitHub Copilot

Programming has always been a mixture of science, engineering, and creativity. Creating new programs and being able to instruct computers to do something has always been something that was considered worth paying for – that’s how all programmers make their living. There have been attempts to automate programming and to support smaller tasks – for example, provide programmers with suggestions on how to use a specific function or library method.

Good programmers, however, can make programs that last and that are readable for others. They can also make reliable programs that work without maintenance for a long period. The best programmers are the ones who can solve very difficult tasks and follow the principles and best practices of software engineering.

In 2020, something happened – GitHub Copilot entered the stage and showed that automated tools, based on large language models...

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