LEADER 04398nam 22006255 450 001 9910736012003321 005 20230729073433.0 010 $a9781484296424 010 $a1484296427 024 7 $a10.1007/978-1-4842-9642-4 035 $a(MiAaPQ)EBC30669121 035 $a(Au-PeEL)EBL30669121 035 $a(DE-He213)978-1-4842-9642-4 035 $a(PPN)272257575 035 $a(CKB)27878832400041 035 $a(Perlego)4515936 035 $a(EXLCZ)9927878832400041 100 $a20230729d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMLOps Lifecycle Toolkit $eA Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems /$fby Dayne Sorvisto 205 $a1st ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (285 pages) 311 08$aPrint version: Sorvisto, Dayne MLOps Lifecycle Toolkit Berkeley, CA : Apress L. P.,c2023 9781484296417 327 $aChapter 1: Introduction to Machine Learning Engineering -- Chapter 2: Developing Stochastic Systems -- Chapter 3: Tools for Data Science Developers -- Chapter 4: Infrastructure for MLOps -- Chapter 5, Building Training Pipelines -- Chapter 6: Building Inference Pipelines -- Chapter 7: Deploying Stochastic Systems -- Chapter 8: Data Ethics -- Chapter 9: Case Studies By Industry. 330 $aThis book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will guide you through the fundamentals of technical decision making, including planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of data science. MLOps Lifecycle Toolkit walks you through the principles of software engineering, assuming no prior experience. It addresses the perennial ?why? of MLOps early, along with insight into the unique challenges of engineering stochastic systems. Next, you?ll discover resources to learn software craftsmanship, data-driven testing frameworks, and computer science. Additionally, you will see how to transition from Jupyter notebooks to code editors, and leverage infrastructure and cloud services to take control of the entire machine learning lifecycle. You?ll gain insight into the technical and architectural decisions you?re likely to encounter, as well as best practices for deploying accurate, extensible, scalable, and reliable models. Through hands-on labs, you will build your own MLOps ?toolkit? that you can use to accelerate your own projects. In later chapters, author Dayne Sorvisto takes a thoughtful, bottom-up approach to machine learning engineering by considering the hard problems unique to industries such as high finance, energy, healthcare, and tech as case studies, along with the ethical and technical constraints that shape decision making. After reading this book, whether you are a data scientist, product manager, or industry decision maker, you will be equipped to deploy models to production, understand the nuances of MLOps in the domain language of your industry, and have the resources for continuous delivery and learning. You will: Understand the principles of software engineering and MLOps Design an end-to-end machine learning system Balance technical decisions and architectural trade-offs Gain insight into the fundamental problems unique to each industry and how to solve them. 606 $aMachine learning 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aC++ (Computer program language) 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aPython 606 $aC++ 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 0$aC++ (Computer program language). 615 14$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aPython. 615 24$aC++. 676 $a006.31 700 $aSorvisto$b Dayne$01380164 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736012003321 996 $aMLOps Lifecycle Toolkit$93421449 997 $aUNINA