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MLOps and Machine Learning Systems: A Professional Guide to Building and Managing Production AI
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.82 kg
0.82 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Have you ever built a machine learning model that worked perfectly on your laptop, only for it to fall apart the moment it reached production? Have you ever wondered why training a model feels exciting, but deploying, monitoring, and maintaining it feels frustrating and unpredictable? What if the real challenge in AI is not building models, but keeping them reliable, scalable, and valuable in real-world environments? This is the conversation MLOps and Machine Learning Systems: A Professional Guide to Building and Managing Production AI by Jim J. Criswell invites you into. This book does not lecture you. It talks with you. It asks the same questions serious practitioners eventually face and then guides you toward practical, professional answers. Have you ever asked yourself how real organizations move from experimental notebooks to dependable production systems? Why do models silently degrade over time? How do teams manage data, models, and pipelines without confusion? What does production-ready AI really mean beyond buzzwords? How do you scale AI systems without sacrificing reliability or trust? This book addresses those questions directly. Training a model is only the beginning. What happens after deployment? Who monitors performance? How do you detect data drift before it causes damage? How do you retrain models safely and automatically? How do you build systems that improve over time instead of breaking? Inside these pages, you are guided through the full lifecycle of real-world machine learning systems—from data ingestion and experimentation to deployment, monitoring, governance, and long-term maintenance. The focus is not theory alone, but professional practice. You will begin to see machine learning not as isolated models, but as living systems that require structure, discipline, and collaboration. Systems that must work today, adapt tomorrow, and remain trustworthy over time. If you have struggled with the gap between data science and engineering, this book speaks directly to that challenge. It explains how machine learning, software engineering, infrastructure, and operations come together into a unified workflow. If you are tired of fragile pipelines and one-off solutions, you will learn how mature teams design reproducible pipelines, automate responsibly, and scale without chaos. If you want to think like a professional responsible for production AI, not just experiments, this guide will change how you design, deploy, and manage intelligent systems. This book is for data scientists moving into production, machine learning engineers refining their systems thinking, software engineers entering AI, and technical leaders responsible for deploying and maintaining AI at scale. It does not promise shortcuts. It does not sell hype. It prepares you for reality. By the end, you will not just understand production machine learning as a concept—you will think in terms of systems, trade-offs, workflows, and long-term sustainability. So ask yourself one final question: Are you ready to stop treating machine learning as an experiment and start treating it as a professional, production-grade system? If you are serious about building AI that actually works in the real world, this book is your guide. Start reading today and take control of your machine learning systems from first experiment to long-term success.
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U$S 48,73
49% OFF
U$S 24,99
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