Custom classifiers
Image, text or tabular models trained from pretrained backbones with torchvision or Hugging Face Transformers.
Languages and frameworks
PyTorch development.
PyTorch is the main framework for building and training neural networks, favoured for its flexible, Python-native design. We use it when an off-the-shelf model is not enough and you need training, fine tuning or custom architectures.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with PyTorch
Where it fits.
Parameter-efficient fine-tunes
LoRA adapters via PEFT on language models, trained on your examples and evaluated against a held-out set.
Vision pipelines
Detection and segmentation models with augmentation, tracked experiments and exportable weights.
Optimised serving
Models exported through TorchScript or ONNX, or served with TorchServe or Triton, so inference meets your latency needs.
When it is the right choice
Choose PyTorch when
- →You need to train or tune a model on your own data.
- →The research community already has a reference implementation in PyTorch.
- →You want flexibility to customise the training loop.
Worth knowing
Honest trade-offs.
- →Training needs GPUs and clean labelled data, and data work usually outweighs modelling work.
- →Getting from a notebook to a monitored, versioned service takes MLOps effort that is easy to underestimate.
Often paired with
Hugging FaceCUDAONNXMLflowWeights and BiasesDocker
Related services
Services that use it.
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
Do we need to train a model at all?
Often not. We first test hosted models and retrieval, and recommend training only when those cannot reach the accuracy, cost or privacy level you need.
How much data do we need?
It depends on the task and starting model. Fine tuning a pretrained model can work with far fewer examples than training from scratch, and we check with a small pilot.
PyTorch or TensorFlow?
PyTorch for most new work because of its ecosystem and flexibility. TensorFlow still makes sense for existing systems and some mobile or browser deployments.
What do you build with PyTorch?
Model training, Fine tuning, Computer vision, NLP, and related work. We choose PyTorch when it is the right fit for the product, the team and the budget.
Can you work with our existing PyTorch code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than PyTorch.
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.