Overview

Transform your Machine Learning Operations (MLOps) projects into reliable and scalable data products that meet the complex demands of data science. This practical guide is for data scientists, machine learning engineers, data leaders, and analytics professionals who want to move beyond notebooks, experiments, and one-time models. Analyze the real reason so many machine learning projects fail, and you will find that the problem is often not the algorithm. It is the data pipeline, the deployment process, the missing monitoring, the weak governance, or the lack of business ownership. This book shows how to treat models as living data products that must be designed, deployed, monitored, improved, and trusted. Explore the full MLOps lifecycle, from data strategy and data contracts to model engineering, CI/CD pipelines, cloud infrastructure, model observability, and production machine learning. Design systems that can handle schema changes, data drift, feature drift, silent failures, unreliable data feeds, and changing business needs. Apply practical thinking to modern data platforms, data warehouses, data lakes, lakehouses, streaming architecture, automated retraining, model registries, and the tools that help data teams build dependable AI systems. Evaluate the next frontier of applied AI with chapters on LLMOps, generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), hallucination monitoring, explainable AI (XAI), Human-in-the-Loop (HITL) systems, and responsible AI governance. Create better enterprise AI applications by understanding how large language models change the deployment game while still requiring the same discipline, testing, observability, cost management, and accountability that define strong MLOps. Assess your role not just as a model builder, but as an owner of business outcomes. The Deployed Data Scientist helps readers connect data science, machine learning, data governance, AI strategy, model deployment, cloud architecture, and business value into one practical roadmap. Whether you are building your first production model or leading a team responsible for enterprise AI, this book gives you the mindset, methods, and language to turn data science into systems that work.

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