Lauchande Machine Learning Engineering with MLflow.

Manage the end-to-end machine learning life cycle with MLflow
1. Auflage 2021
ISBN: 978-1-80056-169-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 248 Seiten

No detailed description available for "Machine Learning Engineering with MLflow.".

Autoren/Hrsg.

Lauchande, Natu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie

Inhaltsverzeichnis

Table of Contents - Introducing MLflow
- Your Machine Learning Project
- Your Data Science Workbench
- Experiment Management in MLflow
- Managing Models with MLflow
- Introducing ML Systems Architecture
- Data and Feature Management
- Training Models with MLflow
- Deployment and Inference with MLflow
- Scaling Up Your Machine Learning Workflow
- Performance Monitoring
- Advanced Topics with MLflow

Über Autor(innen)

Lauchande Natu:
Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.