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.
Apress Media LLC., 2021. — 338 p. — ISBN13: 978-1-4842-6548-2. 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 to basic data...
BPB Publications, 2022. — 552 p. — ISBN: 9789389898514. An insightful journey to MLOps, DevOps, and Machine Learning in the real environment. Key Features Extensive knowledge and concept explanation of Kubernetes components with examples. An all-in-one knowledge guide to training and deploy ML pipelines using Docker and Kubernetes. Includes numerous MLOps projects with access...
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: 978-93-55519-498. 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, 2022. - 529 p. - ISBN: 1803247592. Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle. Key Features Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more. Use container and serverless services to solve a variety of ML...
2nd. ed. - Birmingham: Packt Publishing, 2023. - 460 p. - ISBN: 1837631964. Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems. Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain. Key...
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 Publications Co., 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...
2nd Edition. — Packt Publishing, 2024. — 887 p. ISBN: 978-1-80512-250-0. Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS. Key Features Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling. Apply risk management techniques in the ML lifecycle and design...
BPB Publications, 2023. — 469 p. Deploy, manage, and scale Machine Learning models with MLOps effortlessly. Key Features. Explore several ways to build and deploy ML models in production using an automated CI/CD pipeline. Develop and convert ML apps into Android and Windows apps. Learn how to implement ML model deployment on popular cloud platforms, including Azure, GCP, and...
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...
Apress Media LLC, 2023. — 285 p. — ISBN-13: 978-1-4842-9641-7. 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...
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...
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