What is the core process of AI deployment?

The core process of AI deployment involves the necessary steps to move a trained machine learning model from development into a production environment where it can deliver predictions for real-world business applications. It focuses on operationalizing AI models effectively.

Key phases include setting up the deployment environment, integrating the model into existing applications or workflows, establishing robust monitoring for performance and drift, and implementing a lifecycle management strategy for retraining and updates. This process requires careful consideration of infrastructure, security, scalability, and maintaining model reliability. Ensuring seamless integration with business systems is critical.

The core deployment process enables organizations to realize tangible value from AI investments by making models accessible to end-users or downstream processes. It ensures scalability, continuous performance tracking to detect degradation, and facilitates iterative improvement through feedback loops and retraining, ultimately driving efficiency and informed decision-making.