Flexible research to production
Popular for building and fine-tuning modern AI models.
AI & Data Science
PyTorch
Flexible model development for applied AI solutions.
PyTorch
PyTorch is well suited to iterative model development and modern AI workloads. We use it when the problem calls for custom modelling, while planning data quality, evaluation, deployment, and monitoring from the start.
Useful for AI work that benefits from flexible experimentation, custom neural architectures, or established research implementations.
Planning your implementation
A technology choice is a delivery decision.
We plan the work needed to build, operate and improve the application, not just the first set of features.
- 01
Review the requirements
We validate the dataset, baseline, evaluation design, compute needs, deployment target, safety constraints, and the cost of ongoing inference.
- 02
Validate the important assumptions
Use a focused prototype or a representative workflow where a technical uncertainty could affect scope. Agree the acceptance checks before building the full solution.
- 03
Plan the handover
Reproducible training code, model artefacts, evaluation reports, deployment instructions, and monitoring ownership.
Why PyTorch
Why we use PyTorch.
Where PyTorch adds the most value in the projects we deliver.
Pythonic
Natural to write and debug.
Strong ecosystem
Many pre-trained models and libraries.
Widely adopted
Used across industry and research.
Questions
Common questions.
Quick answers to what people often ask us about PyTorch.
PyTorch or TensorFlow?
Both are excellent. We choose based on the model, deployment target and existing tools.
Do we need to train our own model?
Often not. Many needs can be met with existing models or generative AI, which is faster and cheaper.
AI & Data Science
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Let’s talk
Planning a project with PyTorch?
Tell us what you want to achieve and the right team will get back to you with practical next steps.