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This comprehensive chapter delves into the intricacies of model deployment in machine learning, emphasizing its importance in extracting real-world value from AI. Exploring various stages like model versioning, scaling, and monitoring, it offers an array of tools and platforms that can automate these steps. It underlines that a good model deployment goes beyond mere creation or training, instead turning the model into a valuable application for decision-making, scalability, and continuous improvement. Several popular deployment tools like TensorFlow Serving, Flask, Django, Docker, Kubernetes, and various cloud services are analyzed. The text also shares best practices for successful deployment, including problem understanding, version control, rigorous testing, performance monitoring, security prioritization, infrastructure consideration, open communication, and a rollback plan.