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

Why subscribe?

23. Other Books You May Enjoy

The UI of an ML model

A UI serves as the bridge between the intricate complexities of ML algorithms and the end users who interact with the system. It is the interactive canvas that allows users to input data, visualize results, control parameters, and gain insights from the ML model’s outputs. A well-designed UI empowers users, regardless of their technical expertise, to harness the potential of ML for solving real-world problems.

Effective UIs for ML applications prioritize clarity, accessibility, and interactivity. Whether the application is aimed at business analysts, healthcare professionals, or researchers, the interface should be adaptable to the user’s domain knowledge and objectives. Clear communication of the model’s capabilities and limitations is vital, fostering trust in the technology and enabling users to make informed decisions based on its outputs. Hence my next best practice.

Best practice #66

Focus on the user task when designing the...