MLOps leverage DevOps practices with machine learning processes to reliably develop, deploy, and manage machine learning models.MLOps controls automation processes, monitoring models, control versions, and other aspects necessary to effectively run machine learning models in a production environment.
O’Reilly Media, Inc., 2021. — 492 p. — ISBN: 978-1-098-10301-9 2021-09-14: First Release 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...
O’Reilly Media, Inc., 2024. — 377 p. — ISBN-13: 978-1-098-13658-1. With the demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine-learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine...
BPB Publications, 2024. — 286 p. — ISBN: 978-9355519494. Harness the power of MLOps for managing real-time Machine Learning project cycles. MLOps is the intersection of DevOps, data engineering, and Machine Learning. Working in the field of machine learning is highly dependent on ever-changing data, whereas MLOps is needed to deliver excellent ML and AI results. This book...
Packt Publishing, 2021. — 276 p. — ISBN: 1801079250, 9781801079259. Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering...
Manning PublicationsCo., 2022. — 344 p. — ISBN: 978-1617297762. MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also...
O'Reiily, 2020. - 186 p. - ISBN: 1492083291 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...
Comments