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MLOps Lifecycle Toolkit
Dayne Sorvisto (Broschiert, Englisch)
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Beschreibung
This 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. What You Will Learn Understand the principles of software engineering and MLOps Design an end-to-endmachine learning system Balance technical decisions and architectural trade-offs Gain insight into the fundamental problems unique to each industry and how to solve them Who This Book Is For Data scientists, machine learning engineers, and software professionals. 53,49 €
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Technische Daten
Erscheinungsdatum
30.07.2023
Sprache
Englisch
EAN
9781484296417
Herausgeber
APRESS
Sonderedition
Nein
Autor
Dayne Sorvisto
Seitenanzahl
269
Auflage
1
Einbandart
Broschiert
Buch Untertitel
A Software Engineering Roadmap for Designing, Deploying, and Scaling Stochastic Systems
Schlagwörter
Data Science, Machine Learning, DevOps, MLOps, Python, Business Intelligence
Thema-Inhalt
UYQM - Maschinelles Lernen
UYQ - Künstliche Intelligenz
UMX - Programmier- und Skriptsprachen, allgemein
Höhe
235 mm
Breite
15.5 cm
Transparenz & Sicherheit
Hersteller: Apress, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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