
Practical Deep Learning at Scale with MLflow
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Persons
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
- 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
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File format: ePUB
Copy protection: without DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use a reader that can handle the file format ePUB, such as Adobe Digital Editions or FBReader – both free (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePUB works well for novels and non-fiction books – i.e., 'flowing' text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook does not use copy protection or Digital Rights Management
For more information, see our eBook Help page.