Operationalizing AI involves moving AI models and applications out of the lab and into real-world use reliably, securely, and at scale. Organizations must establish robust engineering practices for deploying, securing, scaling, governing, and supporting AI. This chapter provides a guide applicable across industries and organizations for effectively integrating AI into production and managing it throughout its lifecycle. The approach emphasizes automation, monitoring, risk mitigation, and continuous improvement, ensuring AI systems remain efficient, secure, and trustworthy over time.