Practical Deep Learning at Scale with MLflow
Bridge the gap between offline experimentation and online production
1st Edition
Published on 3. June 2024
288 pages
978-1-80324-222-4 (ISBN)
Description
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Persons
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.
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.
Content
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
- 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
