Управление операциями машинного обучения (MLOps)

BPB Publications, 2024. — 286 р. — ISBN: 978-9355519494. Harness the power of MLOps for managing real time Machine Learning project cycle. 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...

O’Reilly Media, Inc., 2024. — 377 р. — ISBN-13: 978-1-098-13658-1. With 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...

Apress Media LLC, 2023. — 285 р. — ISBN-13: 978-1-4842-9642-4. 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...

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...

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...

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...

Packt Publishing, 2021. — 370 p. — ISBN 978-1800562882. Get up and running with machine learning life cycle management and implement MLOps in your organization Key Features Become well-versed with MLOps techniques to monitor the quality of machine learning models in production Explore a monitoring framework for ML models in production and learn about end-to-end traceability for...

O'Reiily, 2020. - 186p. - 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...

Apress Media LLC., 2021. — 338 p. — ISBN-13 (electronic): 978-1-4842-6549-9. Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. ?This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you...

O’Reilly Media, 2020. — 51 p. — (Early Release) 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 report...