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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

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781837634064
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Miroslaw Staron Miroslaw Staron
Profile icon Miroslaw Staron

Table of Contents (24) Chapters

Preface 1. Part 1:Machine Learning Landscape in Software Engineering
2. Machine Learning Compared to Traditional Software 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

Elements of a production machine learning system

Modern machine learning algorithms are very capable because they use large quantities of data and consist of a large number of trainable parameters. The largest available models are Generative Pre-trained Transformer-3 (GPT-3) from OpenAI (with 175 billion parameters) and Megatron-Turing from NVidia (356 billion parameters). These models can create texts (novels) and make conversations but also write program code, create user interfaces, or write requirements.

Now, such large models cannot be used on a desktop computer, laptop, or even in a dedicated server. They need advanced computing infrastructure, which can withstand long-term training and evaluation of such large models. Such infrastructure also needs to provide means to automatically provide these models with data, monitor the training process, and, finally, provide the possibility for the users to access the models to make inferences. One of the modern ways of providing such...

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