Entropy

Model Deployment and Integration

After training, ML models need to be deployed into production environments where they can make real-time predictions. Deployment strategies include REST APIs, cloud-based services (AWS, Google Cloud, Azure), and edge computing.

Tools such as TensorFlow Serving, TorchServe, and ONNX help deploy models efficiently. Integrating ML models with web applications, mobile apps, or IoT devices ensures they provide value in real-world scenarios.