InferenceOps is an operational framework designed to enhance the deployment, efficiency, and reliability of AI models in production environments, addressing critical challenges in scaling AI applications. The concept emphasizes the importance of inference as a core business capability, moving beyond traditional ML training and evaluation to focus on speed, cost, and reliability. InferenceOps introduces standardized practices for deploying and managing AI models, allowing enterprises to maintain operational control while ensuring models perform effectively at scale. By integrating principles similar to DevOps, InferenceOps facilitates the transition of AI models from development to production, enabling enterprises to navigate the complexities of AI deployment, such as latency issues, cost management, and compliance requirements. The framework provides a balanced approach, combining the convenience of APIs with the control of self-hosted infrastructure, and emphasizes tailored optimization for different workloads, centralized management, and flexible compute access. Through real-world examples, the framework demonstrates its potential to transform AI inference from a cost center into a strategic advantage, offering faster innovation, stronger reliability, and improved unit economics.