Artificial intelligence has progressed from experimental stages to powering various applications across industries, but deploying models into production remains a significant challenge due to requirements for scalable infrastructure, quick response times, and robust monitoring. A diverse range of deployment strategies exists, including shadow testing, canary releases, blue-green rollouts, and serverless inference, each suited to different use cases, risk tolerances, and compliance needs. For instance, real-time services may benefit from shadow deployments, while mission-critical systems might require blue-green deployments for instant rollback capabilities. Federated learning and edge AI are essential for privacy-sensitive applications, while multi-armed bandits facilitate rapid experimentation. Clarifai, a leader in AI, supports these deployment strategies through its compute orchestration platform, which manages models across various environments and ensures observability and rollback features. Experts like Peter Norvig and Genevieve Bell emphasize the importance of infrastructure, transparency, and accountability in AI deployment. Emerging trends such as agentic AI and retrieval-augmented generation introduce new opportunities and risks, necessitating careful planning, monitoring, and governance to ensure reliable and responsible AI deployment.