Model deployment involves transitioning machine-learning models from development to real-world applications where they can be utilized by users and systems to inform decisions. Despite the challenges faced by many organizations, with 90% of models reportedly not reaching production, effective deployment requires a robust foundation and strategic planning. It covers the entire lifecycle from data collection, model training, and evaluation to deployment and monitoring, with the integration of MLOps principles for continuous integration, testing, and deployment. Various deployment strategies are explored, including shadow, A/B, blue-green, canary, and rolling deployments, each with its own advantages and considerations based on risk tolerance, resource availability, and user traffic. The text highlights the importance of packaging and containerization for model consistency, with ONNX and Docker being key components, and emphasizes the role of ethics, compliance, and cost optimization in model deployment. Emerging trends like LLMOps and serverless GPUs are also noted, along with the role of platforms like Clarifai in facilitating seamless deployment through automated solutions, monitoring tools, and flexible deployment options across multiple environments.