Data
A labelled dataset is the starting point. We show the class balance — imbalance is the first thing that breaks models.
Live · ML Engineering · MLOps
Train a model on your data
This is the real technical pipeline behind “train an AI model on company data” — not a slideshow. Pick a sample dataset, hit train, and the model is actually fitted on the server with scikit-learn. You see the train/test split, real accuracy, per-class precision and recall, the confusion matrix, which features mattered most, and you can test live predictions on the model you just trained.
1 · Choose a dataset
How it works
Split
Data is split into train and test sets (stratified) so the score reflects unseen data, not memorisation.
Features
Text becomes TF-IDF vectors; tabular columns are encoded. This is where most real-world effort goes.
Train
A model is fitted — Logistic Regression for text, Random Forest for tabular. This is the actual training step.
Evaluate
Accuracy alone lies. We show per-class precision/recall and the confusion matrix to reveal where it fails.
Predict & iterate
Test the trained model live. In production we add drift monitoring, retraining, and human review — see our services.
Want a model trained on your real data?
We build production training pipelines on your data — labeling, feature engineering, model selection, evaluation gates, drift monitoring, and retraining. From classical ML to fine-tuned LLMs, with the governance to put it in production.
Ready to start
Turn one AI use case into measurable production value.
Book a 30-minute consultation. We will walk through the use case, sketch the value case, and tell you honestly whether we can help.