Liu Practical Deep Learning at Scale with MLflow

Bridge the gap between offline experimentation and online production
1. Auflage 2022
ISBN: 978-1-80324-222-4
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 288 Seiten

No detailed description available for "Practical Deep Learning at Scale with MLflow".

Autoren/Hrsg.

Liu, Yong

Weitere Mitwirkende

Zaharia, Dr. Matei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - Deep Learning Life Cycle and MLOps Challenges
- Getting Started with MLflow for Deep Learning
- Tracking Models, Parameters, and Metrics
- Tracking Code and Data Versioning
- Running DL Pipelines in Different Environments
- Running Hyperparameter Tuning at Scale
- Multi-Step Deep Learning Inference Pipeline
- Deploying a DL Inference Pipeline at Scale
- Fundamentals of Deep Learning Explainability
- Implementing DL Explainability with MLflow

Über Autor(innen)

Liu Yong:
Yong Liu has been working in big data science, machine learning, and optimization since his doctoral student years at the University of Illinois at Urbana-Champaign (UIUC) and later as a senior research scientist and principal investigator at the National Center for Supercomputing Applications (NCSA), where he led data science R&D projects funded by the National Science Foundation and Microsoft Research. He then joined Microsoft and AI/ML start-ups in the industry. He has shipped ML and DL models to production and has been a speaker at the Spark/Data+AI summit and NLP summit. He has recently published peer-reviewed papers on deep learning, linked data, and knowledge-infused learning at various ACM/IEEE conferences and journals.