PyTorch development services
We develop and deliver PyTorch models for engineering teams building task-specific predictions or turning research code into production services. You get a measured baseline, a reproducible model and an application interface, with tests and input monitoring built into the delivery.
01Capabilities
Task-specific models and production delivery
PyTorch is a machine learning framework used from Python for model development and training. Our AI and machine learning development work carries a task model from its data and baseline into a production service.
- Repetition
Classification for repeated decisions
We train task-specific classifiers on labelled examples and measure them against a simple baseline. Model size is selected against your quality and response-time target, with the cost of serving included in the decision.
- Pictures
Vision on operational images
We develop models for damage and defect detection, label reading and counting. For warehouse systems and yard operations, the test data reflects the images the application will receive, including the conditions under which they are captured.
- Prediction
Forecasts that support an action
We model demand, arrival times, pricing and priority around the decision each prediction must support. In automotive and fleet applications, evaluation connects prediction quality to how the result will be used.
- Handover
Research code as a reproducible service
We turn a notebook into versioned training and serving artefacts, with repeatable tests, batching and a rollback path. A stable application interface keeps the production contract explicit as the model changes.
02Fit
Choosing the modelling approach for your decision
We choose a modelling approach from the decision, available data and operating requirements. The comparison keeps a task-specific model alongside rules and hosted services until the same evaluation shows which route fits.
| Your situation | What we recommend |
|---|---|
| A repeated task with labelled examples and a defined quality target | PyTorch trainingWe begin with a simple baseline, then train a limited model and test on held-out data. The delivered evaluation harness stays with the model. |
| A hosted model is a candidate for the same decision | A hosted-model baselineWe evaluate the OpenAI API against your task examples and operating constraints. That result gives the training decision a concrete comparison. |
| Structured rows and columns support a defined business decision | Compare tabular approachesWe evaluate gradient boosting alongside the proposed neural model on the same data split. Quality, runtime and the decision's requirements determine the approach. |
| Training data needs its labels or quality target defined | Data and evaluation prepWe scope the labelling and evaluation work needed to test the task. The training plan follows the data that can be made available and the decision it must support. |
| It has to run on a phone or an embedded board, and the hardware is fixed | Evaluate runtime fitWe test the deployment path against TensorFlow with LiteRT. Framework choice follows the target device, model requirements and maintainability. |
| Reasoning over documents or open-ended writing | Hosted LLM integrationWe assess Claude or Gemini against representative prompts, data requirements and operating cost. The service integration is scoped to the application that will use the answers. |
Scope
We own the success measure and its baseline, the evaluation split and the path from trained model to production interface. Input monitoring keeps the deployed model connected to the data it receives. unicrew has built software since 2012, with 100+ senior in-house engineers across six countries. We are ISO 27001:2022 and ISO 9001:2015 certified, renewed through a multi-stage audit with Quay Audit UK.
03Delivery
From model evaluation to production monitoring
The delivery keeps training results tied to the decision the application must support, from the first baseline to the live input stream.
- Set the target and baselineWe agree the measure that determines success and the simplest rule the model must improve on. That baseline gives training a clear acceptance criterion before the experiment grows.
- Separate training data from evaluationWe split by time or customer to match the way the model will be used. We check for data leakage, including fields that reveal an answer the live system would not have.
- Train a limited model firstWe start from a suitable existing model and adapt a limited layer before expanding training. Held-out results and serving requirements determine whether more training or a larger model is justified.
- Deliver the interface and monitor inputsWe put the model behind a defined service interface and check it alongside real traffic. Input monitoring and model tests run through AI QA and evaluation so changes in the data remain visible after release.
04Stack
The framework and tooling around model delivery
These technologies cover the language, model starting points and packaging that turn model artefacts into an application service.
05Questions
Planning PyTorch model development
These answers cover the training decision, framework choice and resources needed to put a model into service.
Yes. We scope the model baseline, training plan and production interface as an owned engineering engagement.
We agree data access and operating responsibilities with your team. Testing runs through our AI QA and evaluation practice. Our managed teams include unicrew delivery coordination.
Train when evaluation shows that a task-specific model fits your data, quality target and operating requirements better than the available alternatives.
We compare a simple rule and an appropriate hosted model before setting the training scope. Labelled examples, data access and serving costs form part of that comparison. A hosted model remains an implementation option when it meets the same requirements.
The right framework depends on the model, target runtime and code your team will maintain.
We compare available model implementations, the team's existing code and the deployment environment. TensorFlow can be appropriate for new projects as well as existing systems; PyTorch is evaluated against the same requirements. The recommendation includes testing and maintenance after release.
You can train on managed compute when data access and the workload allow it.
We scope training resources separately from serving resources: an experiment's compute needs differ from the requirements of the deployed service. The choice includes memory needs, run duration and the security boundary for the data. Owned hardware is evaluated against those same requirements.
We start by defining the decision the model must support and the baseline it must improve on.
- A representative data sample and how it was collected.
- Data-access restrictions and available labels.
- Existing rules, models or tests for the same decision.
We return a written recommendation on training, evaluation and production delivery. Most engagements start within two to four weeks.
PyTorch development is quoted against the model and delivery scope.
- Time and materials is billed hourly and quoted per project.
- Fixed price is outcome based and quoted per project.
- Team extension is billed monthly per team member.
Scope the model and its production path
Describe the decision and the data available to test it. We will assess the baseline, training scope and delivery requirements, including the interface and monitoring your application needs.