Classification
Route records into known categories with confidence thresholds and exception handling.
Machine Learning Development
Turn labelled business data into classification, ranking and prediction services, with evaluation tied to the decision each model supports.
● Billing■ Delivery○ Review
Uncertain requestHuman triage
Tools & technologies
- Python
- scikit-learn
- Model registry
- Inference API
Give each prediction a decision.
Ranking
Order candidates or content against a defined relevance objective.
Anomaly detection
Flag unusual patterns for investigation rather than treating a score as a verdict.
A model your team can evaluate and operate.
- Reproducible training pipeline
- Held-out evaluation report
- Versioned model endpoint
- Monitoring and retraining runbook
- Reproducible training pipeline
- Held-out evaluation report
- Versioned model endpoint
- Monitoring and retraining runbook
Route a support request
Capability Example
A new ticket needs a queue; past tickets provide reviewed category labels.
Illustrative data. Nothing is sent to an external system.
Example workspace1 / 3
Evaluation set
- Input: refund request
- Reviewed label: billing
- Excluded from training
Split training and evaluation records without leaking later ticket outcomes.
Comparison
- Keyword baseline: returns
- Model suggestion: billing
- Review disagreement
Compare model predictions with a simple keyword baseline.
Routing decision
- Queue: billing review
- Model version: example-v1
- Human can override
Send high-confidence predictions to a queue and uncertain ones to triage.
A suggested category, model version and a visible review path for uncertain tickets.
Follow the record through the next step
Establish the baseline first.
Label quality
Agree what a correct label means and review ambiguous training examples.
Operating cost
Balance inference latency, error cost and retraining frequency against a measured baseline.
Questions before we start.
Do we need a large dataset?
The required volume depends on the task and label coverage. An initial data assessment establishes whether modelling is appropriate.
Is machine learning the same as an LLM application?
No. Many structured prediction tasks use smaller supervised models. We choose around the data and decision, not a language-model default.
Bring your starting point.
Share a sample dataset, its labels and the business cost of an incorrect prediction.
