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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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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

Creating web services over ML models using Flask

In this book, we’ve mostly focused on training, evaluating, and deploying ML models. However, we did not discuss the need to structure them flexibly. We worked with monolithic software. Monolithic software is characterized by its unified, single code base structure where all the functionalities, from the user interface to data processing, are tightly interwoven and operate as one cohesive unit. This design simplifies initial development and deployment since everything is bundled together and they are compiled together. Any change, however minor, requires the entire application to be rebuilt and redeployed. This makes it problematic when the evolution of contemporary software is fast.

On the other hand, web service-based software, which is often associated with microservices architecture, breaks down the application into smaller, independent services that communicate over the web, typically using protocols such as HTTP and REST...

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