Book (Practical) Information science Scaling Case-Proven MLOps Machine Learning Outcomes Prescription / Kiyoshi 山阿 / Mitsuhisa Ōta

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Information Science
KS Technical Book on Science and Engineering / ★ How can the actual use of machine learning really create business value? ★ The first practical guide of "MLOps" is now available, which learns from 3 aspects of technology, process, and culture! It is full of know-how which works well for the troubles in introducing and operating machine learning systems into businesses. Various efforts to scale the results of machine learning in businesses are called MLOps. This book explains the MLOps. Part 1 introduces the overview of MLOps and the technologies, processes, and culture that realize it. The basic knowledge for introducing and operating machine learning systems into businesses can be learned in an easy-to-understand manner. In Part 2, we received from 9 organizations the practices corresponding to Part 1. [Main contents] Part 1 Background and overview of MLOps 1 What is MLOps 11 Background of MLOps 12 Overview of MLOps 2 Technology to realize MLOps 21 Machine learning pipelines 22 Inference systems 23 Technology selection 24 Machine learning execution environment and accelerator 25 Monitoring machine learning systems 26 Data quality control 27 Code quality control 3 Process and culture that supports MLOps 31 Process and culture that supports MLOps 26 Data quality control 27 Code quality control 3 Process and culture that supports MLOps 31 Process and culture that supports MLOps 26 Data quality control 27 Code quality control 3 Process and culture that supports MLOps 31 Process and culture that supports MLOps 31 Process and culture that supports MLOps 31 Development flow of machine learning systems and PoC32 Repeated rapid experiments 33 Collaboration with various stakeholders 34 Monitoring that supports business decision-making 35 Culture that supports MLOps decision-making 35 Culture that supports MLOps Part 2 Practice and prescription of MLOps 4 How to proceed with machine learning projects at DeNA (DeNA Co., Ltd Tamaki Ryuji Fujiwara no Hidehira) 5 How to proceed with content recommendation quickly with a small number of people (CAM Kazuki Hara, Inc.) 6 Monitoring that helps in business decision-making 35 Culture that supports MLOps Part 2 Practice and prescription of MLOps 4 How to proceed with machine learning projects at DeNA (Ryuji Fujiwara, Hidehira) 5 How to proceed with content recommendation quickly with a small number of people (CAM Kazu 澁井 Anryu DeNA Co., Ltd