03277nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072002300184072001600207100007200223245015700295250001800452264005400470300004400524336002600568337002600594338003600620347002400656505033800680520128401018650002202302650004002324650002902364650002202393650001202415650002902427700007602456700007302532710003402605773002602639776003602665776003602701776003602737856004602773979-8-8688-0376-5DE-He21320251030112316.0cr nn 008mamaa240617s2024    xxu|    s    |||| 0|eng d  a97988688037657 a10.1007/979-8-8688-0376-52doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aLuu, Hien.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMLOps with Rayh[electronic resource] :bBest Practices and Strategies for Adopting Machine Learning Operations /cby Hien Luu, Max Pumperla, Zhe Zhang.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXI, 338 p. 111 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1: Introduction to MLOps -- Chapter 2: MLOps Adoption Strategy and Case Studies -- Chapter 3: Feature Engineering Infrastructure -- Chapter 4: Model Training Infrastructure -- Chapter 5: Model Serving -- Chapter 6: Machine Learning Observability -- Chapter 7: Ray Core -- Chapter 8: Ray Air -- Chapter 9: The Future of MLOps.  aUnderstand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll Learn Gain an understanding of the MLOps discipline Know the MLOps technical stack and its components Get familiar with the MLOps adoption strategy Understand feature engineering . 0aMachine learning. 0aPython (Computer program language). 0aArtificial intelligence.14aMachine Learning.24aPython.24aArtificial Intelligence.1 aPumperla, Max.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aZhang, Zhe.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886880375808iPrinted edition:z979886880377208iPrinted edition:z979886881086240uhttps://doi.org/10.1007/979-8-8688-0376-5