Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cyc...
Trends in Representation Theory of Algebras and Related Topics
This book is based on lectures given during a Workshop on Representations of Algebras and Related Topics. Some additional articles are included in order to complete a panoramic view of the main trends of the subject. The volume contains original presentations by leading algebraists addressed to specialists as well as to a broader mathematical audience. The articles include new proofs, examples, and detailed arguments. Topics under discussion include moduli spaces associated to quivers, canonical basis of quantum algebras, categorifications and derived categories, $A$-infinity algebras and functor categories, cluster algebras, support varieties for modules and complexes, the Gabriel-Roiter measure for modules, and selfinjective algebras.
MLOps 도입 가이드
MLOps의 개념부터 도입과 활용까지, 성공적인 머신러닝 운영화를 위한 실용 가이드! 오늘날 데이터 사이언스와 AI는 IT 분야뿐 아니라 제조, 구매, 유통, 마케팅, 반도체, 자동차, 식품 등 산업 전 분야에 걸쳐 기업 생존의 필수 요소로 인식되어 경쟁적으로 도입되고 있다. 이러한 데이터 사이언스와 AI 프로젝트의 핵심에 MLOps가 놓여 있다. 이 책은 비즈니스 환경에서 머신러닝 적용 실무를 담당하는 데이터 분석 팀 또는 IT 운영 팀의 관리자들을 대상으로 한다. MLOps가 새로운 영역이라는 점을 감안하여, MLOps 환경을 성공적으로 구축...
Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cyc...
Biology of Termites: a Modern Synthesis
Biology of Termites, a Modern Synthesis brings together the major advances in termite biology, phylogenetics, social evolution and biogeography. In this new volume, David Bignell, Yves Roisin and Nathan Lo have brought together leading experts on termite taxonomy, behaviour, genetics, caste differentiation, physiology, microbiology, mound architecture, biogeography and control. Very strong evolutionary and developmental themes run through the individual chapters, fed by new data streams from molecular sequencing, and for the first time it is possible to compare the social organisation of termites with that of the social Hymenoptera, focusing on caste determination, population genetics, cooperative behaviour, nest hygiene and symbioses with microorganisms. New chapters have been added on termite pheromones, termites as pests of agriculture and on destructive invasive species.
Information Management and Big Data
This book constitutes the refereed proceedings of the 6th International Conference on Information Management and Big Data, SIMBig 2019, held in Lima, Peru, in August 2019. The 15 full papers and 16 short papers presented were carefully reviewed and selected from 104 submissions. The papers address issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning, web mining.
