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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...
Machine Learning in Production
A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models. Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of bu...
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...
MLOps – Kernkonzepte im Überblick
Erfolgreiche ML-Pipelines entwickeln und mit MLOps organisatorische Herausforderungen meistern Stellt DevOps-Konzepte vor, die die speziellen Anforderungen von ML-Anwendungen berücksichtigen Umfasst die Verwaltung, Bereitstellung, Skalierung und Überwachung von ML-Modellen im Unternehmensumfeld Für Data Scientists und Data Engineers, die nach besseren Strategien für den produktiven Einsatz ihrer ML-Modelle suchen Viele Machine-Learning-Modelle, die in Unternehmen entwickelt werden, schaffen es aufgrund von organisatorischen und technischen Hürden nicht in den produktiven Betrieb. Dieses Buch zeigt Ihnen, wie Sie erprobte MLOps-Strategien einsetzen, um eine erfolgreiche DevOps-Umgebung f...
Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach--Build, Manage, Deploy and Integrate, and Monitor--for creating ML-infused applications within your organiz...
Practical MLOps
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
