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

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781837634064

Length 346 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Miroslaw Staron

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Table of Contents (24) Chapters

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

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Deploying a code completion model as an extension

So far, we’ve learned how to deploy models online and on the Hugging Face hub. These are good methods and provide us with the ability to create a UI for our models. However, these are standalone tools that require manual input and provide an output that we need to use manually – for example, paste into another tool or save to disk.

In software engineering, many tasks are automated and many modern tools provide an ecosystem of extensions and add-ins. GitHub Copilot is such an add-in to Visual Studio 2022 and an extension to Visual Studio Code – among other tools. ChatGPT is both a standalone web tool and an add-in to Microsoft’s Bing search engine.

Therefore, in the last part of this chapter, we’ll package our models as an extension to a programming environment. In this section, we learn how to create an extension to complete code, just like GitHub CoPilot. Naturally, we won’t use the CodeX...

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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

Table of Contents (24) Chapters

Deploying a code completion model as an extension

Authors (1)

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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

Table of Contents (24) Chapters

Deploying a code completion model as an extension

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