How do you deploy a trained neural network model?

Deploying a trained neural network model involves making it accessible for real-world use, typically through an API or embedded system. The process starts by saving the trained model in a format compatible with your deployment environment. Frameworks like TensorFlow, PyTorch, or ONNX provide tools to export models into standardized formats (e.g., TensorFlow SavedModel, TorchScript, or ONNX files). For example, TensorFlow models can be saved using tf.saved_model.save(), which bundles the architecture, weights, and computation graph. Once saved, the model is loaded into a serving environment, such as a web server, cloud service, or edge device, where it processes incoming data and returns predictions.

The next step is setting up an interface to handle requests. This often involves creating a REST API using frameworks like Flask, FastAPI, or specialized tools like TensorFlow Serving. For instance, a Flask app might load the model and expose an endpoint that accepts input data (e.g., images or text), runs inference, and returns JSON-formatted results. To ensure scalability, containerization tools like Docker can package the model and API into a portable image, while orchestration systems like Kubernetes manage multiple instances. Cloud platforms like AWS SageMaker or Google Vertex AI simplify deployment by providing pre-built infrastructure for model hosting, auto-scaling, and monitoring. For edge devices, frameworks like TensorFlow Lite or ONNX Runtime optimize models for performance on resource-constrained hardware.

Finally, monitoring and maintenance are critical for long-term reliability. Logging tools like Prometheus or cloud-native services track metrics such as latency, error rates, and throughput. Versioning systems (e.g., MLflow or DVC) help manage model updates, allowing rollbacks if issues arise. For example, if a new model version degrades performance, traffic can be rerouted to the previous version. Continuous integration pipelines (e.g., GitHub Actions) automate testing and deployment when model updates occur. Security measures like input validation, rate limiting, and authentication (e.g., OAuth) protect the API from misuse. Regular retraining cycles, triggered by data drift detection tools like Evidently AI, ensure the model stays accurate as real-world data evolves.