Staron Machine Learning Infrastructure and Best Practices for Software Engineers

Take your machine learning software from a prototype to a fully fledged software system
1. Auflage 2024
ISBN: 978-1-83763-694-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 346 Seiten

No detailed description available for "Machine Learning Infrastructure and Best Practices for Software Engineers".

Autoren/Hrsg.

Staron, Miroslaw

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse

Inhaltsverzeichnis

Table of Contents - Machine Learning Compared to Traditional Software
- Elements of a Machine Learning Software System
- Data in Software Systems – Text, Images, Code, Features
- Data Acquisition, Data Quality and Noise
- Quantifying and Improving Data Properties
- Types of Data in ML Systems
- Feature Engineering for Numerical and Image Data
- Feature Engineering for Natural Language Data
- Types of Machine Learning Systems – Feature-Based and Raw Data Based (Deep Learning)
- Training and evaluation of classical ML systems and neural networks
- Training and evaluation of advanced algorithms – deep learning, autoencoders, GPT-3
- Designing machine learning pipelines (MLOps) and their testing
- Designing and implementation of large scale, robust ML software – a comprehensive example
- Ethics in data acquisition and management
- Ethics in machine learning systems
- Integration of ML systems in ecosystems
- Summary and where to go next

Über Autor(innen)

Staron Miroslaw:

Miroslaw Staron is a professor of Applied IT at the University of Gothenburg in Sweden with a focus on empirical software engineering, measurement, and machine learning. He is currently editor-in-chief of Information and Software Technology and co-editor of the regular Practitioner's Digest column of IEEE Software. He has authored books on automotive software architectures, software measurement, and action research. He also leads several projects in AI for software engineering and leads an AI and digitalization theme at Software Center. He has written over 200 journal and conference articles.