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Training a deep learning model is only half the challenge. To create real-world impact, models must be deployed — packaged, optimised, and served so that applications and users can make predictions in production.
| Stage | Description | Tools |
|---|---|---|
| Training | Train and validate the model | PyTorch, TensorFlow, GPUs |
| Export | Convert the model to a deployment-friendly format | ONNX, TorchScript, SavedModel |
| Optimise | Reduce model size and latency | Quantisation, pruning, distillation |
| Serve | Host the model behind an API or embed it | FastAPI, TorchServe, TF Serving |
| Monitor | Track performance, data drift, and errors | MLflow, Prometheus, Grafana |
TorchScript converts a PyTorch model to a format that can run without Python.
import torch
# Method 1: Tracing (works for models without control flow)
model.eval()
example_input = torch.randn(1, 3, 224, 224)
traced_model = torch.jit.trace(model, example_input)
traced_model.save('model_traced.pt')
# Method 2: Scripting (handles control flow like if/else)
scripted_model = torch.jit.script(model)
scripted_model.save('model_scripted.pt')
# Load in any environment (no Python needed)
loaded = torch.jit.load('model_traced.pt')
output = loaded(example_input)
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