PyTorch experts
Research friendly PyTorch workflows that translate into scalable, observable machine learning products.
We enable teams to experiment quickly, manage data and ship production ready PyTorch models across cloud and edge.
What is PyTorch
PyTorch is an open source deep learning framework popular with researchers for its dynamic computation graph and ease of use.
It powers breakthrough models across vision, NLP and generative AI, with strong community support.
We help you move from notebooks to robust PyTorch services with testing, monitoring and governance.
Rapid prototyping with PyTorch Lightning, Hugging Face and custom architectures.
Distributed training on GPUs with mixed precision and efficient data loaders.
Deploy models via TorchServe, ONNX Runtime or custom APIs.
Quantisation, pruning and distillation to meet latency and cost targets.
Why PyTorch
- Favoured by research and open source communities
- Supports cutting edge model architectures
- Flexible deployment options across cloud and edge
- Integrates with LLM and diffusion model ecosystems
PyTorch projects
We help organisations deliver sophisticated AI solutions using PyTorch.
Diffusion models, GANs and transformers tailored to your data.
Detection, tracking and segmentation for industrial and retail use cases.
Custom language models, summarisation and classification systems.
Graph and sequence models powering personalised experiences.
Optimised PyTorch models for mobile, embedded and robotics platforms.
Experiment tracking, dataset versioning and reproducibility workflows.
When PyTorch is the right choice
- Research teams working on novel architectures
- Products requiring rapid experimentation and iteration
- Organisations building generative or multimodal AI
- Teams who need flexibility across cloud, edge and accelerator hardware
When to consider alternatives
- TensorFlow may suit teams prioritising mature production pipelines.
- Classical ML libraries could cover simpler predictive tasks.
- Managed AI services might deliver value faster for standard use cases.
- LLM APIs could replace bespoke models for conversational experiences.
PyTorch vs TensorFlow
| Criterion | PyTorchResearch | TensorFlowEcosystem |
|---|---|---|
| Development style | Dynamic eager execution | Graph based execution with optional eager mode |
| Community | Open source research community | Large enterprise community |
| Deployment | TorchServe, ONNX, custom | TFX, Serving, Vertex AI |
| Use cases | Experimental, generative and research heavy | Production scale and enterprise |
| Edge support | TorchScript and Mobile | TensorFlow Lite |
We guide you to the framework that suits your experimentation pace and production needs.
Deliver with PyTorch
We provide research enablement, engineering and MLOps to launch PyTorch powered products.
No obligation. We keep experimental data secure and delete it on request.