Deploying an NLP model involves making it available for practical use through APIs or applications. The process includes:
- Model Packaging: Save the trained model in a deployable format (e.g., .pickle for Scikit-learn, .pt for PyTorch, or .h5 for TensorFlow). Frameworks like Hugging Face also support model export to formats like ONNX.
- API Development: Wrap the model in a RESTful API using Flask, FastAPI, or Django. This allows the model to handle HTTP requests for inference.
- Containerization: Use Docker to package the model, dependencies, and API for consistent deployment across environments. Docker ensures portability and scalability.
- Hosting and Scaling: Deploy the containerized application on cloud platforms like AWS, Google Cloud, or Azure. Kubernetes can be used for scaling and orchestration.
Additional considerations include setting up monitoring (e.g., Prometheus, Grafana), logging, and automated retraining pipelines for continuous improvement. Tools like Hugging Face Inference API and TensorFlow Serving simplify deployment workflows. Successful deployment ensures the model is accessible, efficient, and reliable for real-world applications.