The process of integrating AI models into production environments where they can deliver business value
AI Deployment represents the critical bridge between developing models in controlled environments and implementing them in real-world operational settings where they can generate tangible business value. This multifaceted process extends far beyond technical implementation to encompass infrastructure provisioning, monitoring systems, integration with existing workflows, and change management strategies. Successful deployment requires addressing challenges across the ML lifecycle—from model serving and scalability to drift detection, explainability, and ongoing maintenance. Organizations must navigate the complexity of deploying models across diverse environments, from cloud platforms to edge devices, while maintaining consistent performance and security. What distinguishes mature AI deployment practices is the emphasis on operationalizing models as reliable, manageable services rather than one-off technical achievements, creating sustainable systems that can evolve alongside changing business requirements and data distributions.