Reliable AI systems are built, not assumed. Model validation is the discipline that determines whether a model is ready for real-world use. Practical Model Validation for Teams provides a structured, engineering-focused approach to testing and validating machine learning models before and after deployment. Written for ML engineers and QA teams, this book translates abstract "trust" requirements into concrete tests, benchmarks, and acceptance criteria. The book focuses on operational validation—not academic evaluation—and emphasizes repeatability, transparency, and production readiness. Readers will learn how to: Design validation suites that reflect real-world usage Define unit, integration, and system-level tests for models Establish performance, fairness, and robustness benchmarks Run controlled A/B experiments and shadow deployments Set clear acceptance criteria for production approval Integrate validation into CI/CD and MLOps pipelines This guide helps teams reduce deployment risk, improve model reliability, and create shared standards for AI quality across engineering and governance stakeholders.