Source: https://daphnislabs.com/services/machine-learning

# Machine Learning Development

Turn labelled business data into classification, ranking and prediction services, with evaluation tied to the decision each model supports.

[Discuss your project](https://daphnislabs.com/contact?service=machine-learning) [Try the example](https://daphnislabs.com/services/machine-learning#worked-example)

**Model evaluation**

Two labelled groups of samples separated by a model decision boundary

● Billing ■ Delivery ○ Review

Uncertain request **Human triage**

## Tools & technologies

-   Python
-   scikit-learn
-   Model registry
-   Inference API

## Give each prediction a decision.

### Classification

Route records into known categories with confidence thresholds and exception handling.

### 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**

## Route a support request

Capability Example

A new ticket needs a queue; past tickets provide reviewed category labels.

### Separate

### Score

### Route

Illustrative data. Nothing is sent to an external system.

Example workspace 1 / 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.

[AI Development](https://daphnislabs.com/services/ai-development) [Predictive Analytics Services](https://daphnislabs.com/services/predictive-analytics) [Deep Learning Development](https://daphnislabs.com/services/deep-learning) [AI Data Pipelines & Vector DB](https://daphnislabs.com/services/ai-data-pipelines-vector-db)

## Bring your starting point.

Share a sample dataset, its labels and the business cost of an incorrect prediction.

[Discuss your project](https://daphnislabs.com/contact?service=machine-learning) [Explore related services](https://daphnislabs.com/services/ai-development)