ML TensorFlow / PyTorch

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