Deployment Optimization refers to the process of making the deployment of models to production systems as efficient and effective as possible. This includes minimizing latency, maximizing throughput, and ensuring the model can scale and work robustly in the real-world environment. Techniques might involve fine-tuning computational resources, employing containerization (like Docker), using model compression or quantization, and ensuring compatibility across different platforms. This concept is critical in industries where model performance translates directly to business value.