If You Like To Scale Deep Learning Models Read This Book

How to bridge the gap between offline experimentation and online production

Deep learning has been revolutionizing many areas of computing in the past decade, but good resources for using it in production applications remain scarce. At the same time, practitioners have realized that designing machine learning (ML) applications to be operable, maintainable, and updateable is one of the hardest parts of using ML in production, leading to the new field of MLOps.

Dr. Liu in his book Practical Deep Learning at Scale with MLFlow tackles these issues by showing you how to build robust and maintainable deep learning applications using MLflow, a widely-used open-source MLOps framework, and multiple state-of-the-art methods and software tools.

Dr. 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.