Overview

In most companies, ""let's add machine learning"" quickly turns into confusion: endless vendor pitches, proof-of-concepts that never ship, and models nobody trusts. The real problem isn't a lack of algorithms. It's a lack of clear patterns that connect business problems, data realities, and production constraints. Industry Machine Learning Playbook is a practical field guide for technical leaders inside real organizations-not research labs. Instead of organizing around models or tools, the book is organized around concrete use cases across industry verticals such as healthcare, financial services, manufacturing, energy, retail, and more. For each use case, you see the problem through three lenses: The industry engineer, who describes the problem in operational and business terms. The ML engineer, who turns that problem into a well-posed modeling and data task. The solution architect, who shows how to make the solution work within real systems, compliance requirements, and budgets. You'll learn how to: Decide whether a problem is truly ""machine-learning-shaped"" or solvable with simpler analytics and better processes. Map messy, delayed, or incomplete data into tractable ML tasks such as classification, forecasting, anomaly detection, and recommendation. Reuse patterns across sectors for challenges like churn and retention, fraud and anomaly detection, pricing and bidding, personalization, and demand forecasting. Connect ML work to production architectures, MLOps, and FinOps so that models are monitorable, cost-aware, and maintainable over time. This book is written for technical leaders inside industry verticals: heads of analytics or ML in a business unit, operations and clinical leaders, platform and solution architects, and senior engineers who keep being asked to ""add AI"" to existing systems. If you need a vendor-neutral, sector-aware playbook to structure ML discussions, prioritize the right use cases, and avoid wasting years on the wrong projects, this book gives you the patterns and language to do it.

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