LEADER 03598nam 22006255 450 001 9910865243603321 005 20240617125243.0 010 $a9798868803765$b(electronic bk.) 010 $z9798868803758 024 7 $a10.1007/979-8-8688-0376-5 035 $a(MiAaPQ)EBC31492145 035 $a(Au-PeEL)EBL31492145 035 $a(CKB)32311376500041 035 $a(DE-He213)979-8-8688-0376-5 035 $a(OCoLC)1441719836 035 $a(OCoLC-P)1441719836 035 $a(CaSebORM)9798868803765 035 $a(EXLCZ)9932311376500041 100 $a20240617d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMLOps with Ray $eBest Practices and Strategies for Adopting Machine Learning Operations /$fby Hien Luu, Max Pumperla, Zhe Zhang 205 $a1st ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (342 pages) 300 $aDescription based upon print version of record. 300 $aChapter 4: Model Training Infrastructure 311 08$aPrint version: Luu, Hien MLOps with Ray Berkeley, CA : Apress L. P.,c2024 9798868803758 327 $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. 330 $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 . 606 $aMachine learning 606 $aPython (Computer program language) 606 $aArtificial intelligence 606 $aMachine Learning 606 $aPython 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aPython. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aLuu$b Hien$01060685 701 $aPumperla$b Max$01742737 701 $aZhang$b Zhe$0759945 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910865243603321 996 $aMLOps with Ray$94169429 997 $aUNINA