AI Model Deployment is the process of integrating trained machine learning models into live environments so they can deliver real-time predictions or decisions.
Deployment includes choosing the right serving infrastructure (e.g. REST APIs, cloud functions, containers), monitoring performance, scaling, and managing version control. It’s the bridge between data science and production software.
Tools like TensorFlow Serving, TorchServe, AWS SageMaker, and MLflow simplify the process of hosting models reliably and securely. Key considerations include latency, cost, load balancing, and retraining workflows.
Robust deployment practices ensure that AI models not only work in theory — but deliver value at scale in the real world.
