Deep Learning in Production
PyTorch has become the dominant framework for research and increasingly for production deployment. TensorFlow/Keras remains strong for teams with existing investments or TensorFlow Serving infrastructure. We choose based on the model architecture, the team, and the deployment target — not preference.
Our ML Engineering Stack
- PyTorch Lightning for structured training loops
- Hugging Face Transformers for fine-tuning foundation models
- ONNX for cross-framework model export and optimisation
- Triton Inference Server for high-throughput GPU serving
- MLflow for experiment tracking and model registry
- DVC for dataset versioning