Can we fine-tune a model on our own data?

Usually, and it is the most common thing we do. Modern fine-tuning adapts a small portion of an existing model, so it needs far less data and compute than training from nothing. A few thousand good examples often gets you further than you would expect.

What will training cost?

Less than most people fear for fine-tuning, where a run often costs hundreds rather than thousands. Training from scratch is a different order entirely, which is one reason we rarely recommend it. We estimate GPU hours before committing to anything.

Can our data stay private during training?

Yes. Fine-tuning open models means the data stays inside infrastructure you control, and nothing is sent to a third-party provider. That is frequently the deciding argument for this approach in healthcare, legal, and financial work.

Can a PyTorch model run in our mobile app?

Often, through export to a mobile runtime, though the tooling is less mature than the TensorFlow path. If on-device inference is the main requirement rather than an afterthought, our TensorFlow page covers the better-supported route.

PyTorch AI
Development Services

Stateside Digitals provides PyTorch AI development services, building and adapting machine learning models around your own data, your own vocabulary, and the judgements a general-purpose model cannot make.

PyTorch is the machine learning framework most new work is built in. It behaves like ordinary Python, meaning a developer can step through a model line by line and watch what it is doing, rather than describing a computation in advance and waiting to discover the result. That difference sounds academic and in practice decides how quickly a team can iterate.

For a business, the consequence is reach. When a new technique or open model is published, the PyTorch version appears first and frequently is the only version. Building here means work you fund this quarter can adopt something released next quarter, rather than waiting for someone else to port it into a different framework months afterward.

Faster To Experiment

See What Broke, Immediately

Models can be inspected while running, so a problem gets found in minutes instead of being inferred from numbers at the end of a run.

Every Model Lands Here

New Research Ships In PyTorch

Open models and new techniques are released in PyTorch first, so your project can use them immediately rather than waiting on a port.

Adapt, Don’t Rebuild

Start From A Trained Model

Fine-tuning an existing model on your data costs a fraction of training one, and reaches useful accuracy with far less material.

Domain-Specific Models

Models tuned to your industry’s language, formats, and edge cases, where a general tool keeps getting the specialist details wrong.

Document Understanding

Pulling structured information out of invoices, contracts, claims, and forms, including the messy scanned ones that make rule-based extraction fall over almost immediately.

Custom Search Ranking

Search that understands meaning rather than matching words, tuned on what your users actually clicked rather than generic relevance.

There are three options for most AI features, and the expensive one is rarely the right one. You can call a commercial API and pay per request. You can fine-tune an open model on your own data and run it yourself. Or you can train something from scratch, which almost nobody outside a research lab has a good reason to be doing.

An API wins when the task is general: summarizing, drafting, answering questions about documents you supply. Fine-tuning wins when the work depends on your vocabulary, your formats, or judgements only your business makes, when the data cannot leave your infrastructure, or when per-request costs at your volume exceed running it yourself. We establish which before quoting.

We build with current PyTorch, working through the Hugging Face ecosystem for open models, datasets, and parameter-efficient fine-tuning with LoRA. Training runs on Vertex AI, SageMaker, or rented GPUs depending on cost and data residency. Experiments are tracked in Weights and Biases, and finished models export to ONNX or serve through TorchServe or vLLM.

PyTorch
Python
Hugging Face
Datasets
LoRA
Vertex AI
SageMaker
Weights and Biases
ONNX
TorchServe
vLLM
GPUs

Open Weights Where Possible

Training Costs Estimated First

Data Licensing Checked

Results Reproducible

Open models keep the weights and the data inside infrastructure you control, which is usually what decides the approach in regulated work.

GPU hours are estimated before anything runs, because training spend is easy to underestimate and awkward to stop once it has started.

We check what the training data actually permits, since a model trained on material you were not licensed to use becomes your exposure, not ours.

Seeds, data versions and hyperparameters are recorded, so a result can be rebuilt months later instead of being taken on trust.

Open Weights Where Possible

Training Costs Estimated First

Data Licensing Checked

Results Reproducible

For building and training models, yes, and that is where most of the ecosystem now sits. TensorFlow keeps the advantage in deployment, particularly on mobile devices. We use both and choose by where the finished model has to run rather than by habit.

PyTorch AI
Development Services