Machine Learning Development & Model Training
Custom machine learning for problems off-the-shelf models cannot solve: computer vision, prediction, anomaly detection, signal processing and on-device inference. We follow proper practice end to end: clean data splits, baselines, experiment tracking, honest evaluation and monitoring after deployment.
What’s included
- ✓Problem framing, data audit and a feasibility study before you commit
- ✓Data collection, labelling and cleaning pipelines
- ✓Model training with tracked experiments, baselines and held-out test sets
- ✓Computer vision, time-series forecasting, classification and anomaly detection
- ✓Deployment as an API, in your app, or on-device (edge and embedded inference)
- ✓Monitoring for drift and a retraining plan
Tools we use
PyTorchTensorFlowscikit-learn
Hugging Face
ONNXTensorRT / TFLite
MLflow
Weights & BiasesQuestions
- How much data do we need?
- It depends on the task. We start with a feasibility study on the data you have and tell you honestly whether it is enough.
- Can the model run on our device instead of the cloud?
- Yes. We quantise and export models for edge hardware such as Jetson, Raspberry Pi, Android and microcontrollers, and we write the firmware side too.
- How do we know the model actually works?
- You get a report with results on a held-out test set that was never used in training, plus failure cases, not just a single accuracy number.