PyTorch development for custom deep learning models
PyTorch is an open-source deep learning framework widely used in research and industry. It is the usual choice when you need to train or fine-tune your own neural network, for example for image recognition, language tasks or specialised prediction.
PyTorch is intuitive to write and debug, and most new open models and research code are published for it first, which makes fine-tuning and experimentation faster. It runs on GPUs and exports to formats suitable for production serving. Deep learning needs quality training data, compute and ongoing monitoring, so it is not always justified. For tabular business data, a simpler scikit-learn model is often as accurate and far cheaper; for text tasks, a hosted language model may be quicker.
What we build with PyTorch
- Computer vision and image classification
- Fine-tuning open language models
- Custom NLP models
- Time-series and sequence models
- Model serving and optimisation
PyTorch questions
Do we need our own model or can we use an existing one?
Usually start with an existing pre-trained or hosted model. Training or fine-tuning becomes worthwhile when your task is specialised and you have suitable data.
Should we use PyTorch or TensorFlow?
Both are capable. PyTorch dominates new research and open models; TensorFlow is common in existing production systems and on-device deployment.
What hardware is needed to train a PyTorch model?
Most deep learning training needs GPUs, which can be rented from cloud providers by the hour. Smaller fine-tuning jobs can run on a single GPU, keeping costs modest.
How do you know a trained model is good enough?
We hold back test data the model never sees during training and measure it against metrics agreed with you, such as accuracy, precision and recall, before deployment.
Can you fine-tune an open language model with PyTorch?
Yes. Techniques such as LoRA fine-tune only a small set of extra parameters, which cuts hardware needs while adapting the model to your task.
How are PyTorch models deployed?
We package models behind an API using serving tools, export them to ONNX where useful, and run them on CPU or GPU infrastructure in your own cloud account.
Can PyTorch models run on mobile devices?
Yes. Models can be optimised and exported for on-device use, which keeps data on the phone and works without a connection, though model size must be kept small.
Who owns a model you train for us?
You do. The training code, trained weights and data pipelines are delivered to your repositories and cloud account, subject to the licence terms of any base model used.
How do you keep inference costs and latency down?
We use techniques such as quantisation, batching and smaller distilled models, and choose hardware based on measured throughput rather than guesswork.
How do you keep a model accurate after launch?
We monitor predictions and input data for drift, collect feedback on errors and retrain on fresh data when performance drops.
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Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.