Raj Engineering MLOps

Rapidly build, test, and manage production-ready machine learning life cycles at scale
1. Auflage 2021
ISBN: 978-1-80056-632-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 370 Seiten

No detailed description available for "Engineering MLOps".

Autoren/Hrsg.

Raj, Emmanuel

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Webprogrammierung

Inhaltsverzeichnis

Table of Contents - Fundamentals of MLOps Workflow
- Characterizing your Machine learning problem
- Code Meets Data
- Machine Learning Pipelines
- Model evaluation and packaging
- Key principles for deploying your ML system
- Building robust CI and CD pipelines
- APIs and microservice Management
- Testing and Securing Your ML Solution
- Essentials of Production Release
- Key principles for monitoring your ML system
- Model Serving and Monitoring
- Governing the ML system for Continual Learning

Über Autor(innen)

Raj Emmanuel:
Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.