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Computer Vision / ML / DL
Custom deep learning and neural network development
Some problems need a trained neural network rather than a rules-based approach, especially with images, audio, text or complex signals. We design, train, optimise and deploy custom models, and we say so when a smaller model would do.
What we build
Deep Learning Development: what you get
- Model selection and fine-tuning of proven architectures
- Training pipelines with experiment tracking
- Handling of small or imbalanced datasets and data augmentation
- Model compression and speed optimisation for production
- Evaluation reports your engineers and managers can read
- Packaging as an API, container or on-device model
This is a good fit if…
- Images, audio, text or sensor signals are central to the problem
- Off-the-shelf models do not reach the accuracy you need
- The model must run fast or on limited hardware
Typical uses
- Classify images that simpler methods cannot separate
- Process audio or sensor signals for events
- Speed up a model so it runs on a small device
How it works
A simple process, no surprises
- 1
Discovery
We review your data, accuracy needs and the hardware the model must run on.
- 2
Scope and plan
We choose a starting architecture and agree how success will be measured.
- 3
Build and review
We fine-tune, train and test, tracking each experiment so results can be reproduced.
- 4
Deliver and support
We optimise for speed, package the model and hand over the training pipeline.
Technologies we use for this
- PyTorch
- TensorFlow
- ONNX
- TensorRT
- CUDA
- Docker
Reviews
What clients say
★★★★★
“They took a task my team repeated every week and turned it into a single button. Clear communication throughout, and the handover documentation meant we could use it straight away”
FAQ
Deep Learning Development: common questions
Do we need to start from scratch?+−
Rarely. We usually fine-tune an established model on your data, which is faster and cheaper than training from scratch.
Is a GPU required?+−
Training usually needs one. We can use cloud GPUs so you do not need to buy hardware, and optimise the final model to run on what you have.
Who owns the trained model?+−
Ownership is agreed in the written proposal. Many clients want full ownership of models trained on their data.
Related services
Need deep learning development?
Describe your data and what the model must decide. We will tell you whether a neural network is the right tool.
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