MLOps объединяет практики DevOps с процессами машинного обучения для обеспечения надежной разработки, развертывания и управления моделями машинного обучения.В рамках MLOps обеспечивается автоматизация процессов, мониторинг моделей, управление версиями и другие аспекты, необходимые для эффективной работы с моделями машинного обучения в производственной среде.
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, 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...
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...
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. — 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...
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...
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