# Model Card Template

## Overview

Template for documenting machine learning models following best practices.

---

## Model Card Document

```markdown
# Model Card: [Model Name]

## Model Details

### Basic Information

| Field      | Value                             |
| ---------- | --------------------------------- |
| Model Name | [Name]                            |
| Version    | [X.Y.Z]                           |
| Type       | [Classification/Regression/etc.]  |
| Framework  | [TensorFlow/PyTorch/scikit-learn] |
| Date       | YYYY-MM-DD                        |
| Authors    | [Team/Names]                      |

### Description

[Brief description of what the model does]

### Intended Use

- **Primary Use Cases**: [What it's designed for]
- **Intended Users**: [Who should use it]
- **Out-of-Scope Uses**: [What it shouldn't be used for]

---

## Model Architecture

### Overview

[Description of model architecture]

### Inputs

| Name      | Type  | Shape | Description        |
| --------- | ----- | ----- | ------------------ |
| feature_1 | float | (1,)  | Transaction amount |
| feature_2 | int   | (1,)  | Category code      |

### Outputs

| Name        | Type  | Shape        | Description         |
| ----------- | ----- | ------------ | ------------------- |
| prediction  | int   | (1,)         | Class label         |
| probability | float | (n_classes,) | Class probabilities |

### Hyperparameters

| Parameter     | Value |
| ------------- | ----- |
| learning_rate | 0.001 |
| batch_size    | 32    |
| epochs        | 100   |

---

## Training Data

### Dataset Description

| Field      | Value            |
| ---------- | ---------------- |
| Name       | [Dataset name]   |
| Version    | [Version]        |
| Size       | [N samples]      |
| Date Range | [Start] to [End] |

### Data Distribution

| Feature | Distribution |
| ------- | ------------ |
| Class 0 | 85%          |
| Class 1 | 15%          |

### Preprocessing

- [Step 1]: [Description]
- [Step 2]: [Description]

### Data Splits

| Split      | Size | Purpose               |
| ---------- | ---- | --------------------- |
| Train      | 70%  | Model training        |
| Validation | 15%  | Hyperparameter tuning |
| Test       | 15%  | Final evaluation      |

---

## Evaluation

### Metrics

| Metric    | Value | Threshold |
| --------- | ----- | --------- |
| Accuracy  | 0.95  | > 0.90    |
| Precision | 0.92  | > 0.85    |
| Recall    | 0.88  | > 0.80    |
| F1 Score  | 0.90  | > 0.85    |
| AUC-ROC   | 0.97  | > 0.90    |

### Confusion Matrix
```

              Predicted
              0     1

Actual 0 850 50
1 30 120

```

### Performance by Subgroup
| Subgroup | Accuracy | Size |
|----------|----------|------|
| Group A | 0.96 | 400 |
| Group B | 0.94 | 350 |
| Group C | 0.93 | 250 |

---

## Fairness & Bias

### Evaluation
| Metric | Group A | Group B | Threshold |
|--------|---------|---------|-----------|
| TPR | 0.89 | 0.87 | Δ < 0.05 ✅ |
| FPR | 0.08 | 0.09 | Δ < 0.05 ✅ |
| PPV | 0.91 | 0.89 | Δ < 0.05 ✅ |

### Mitigation Steps
- [Step taken to address bias]

### Known Limitations
- [Limitation 1]
- [Limitation 2]

---

## Ethical Considerations

### Potential Risks
- [Risk 1]: [Mitigation]
- [Risk 2]: [Mitigation]

### Use Cases to Avoid
- [Should not be used for X]

---

## Deployment

### Requirements
```

python>=3.9
tensorflow==2.12.0
numpy==1.24.0

```

### Resource Requirements
| Resource | Minimum | Recommended |
|----------|---------|-------------|
| CPU | 2 cores | 4 cores |
| Memory | 2 GB | 4 GB |
| GPU | - | NVIDIA T4 |

### Latency
| Percentile | Latency |
|------------|---------|
| p50 | 15ms |
| p95 | 45ms |
| p99 | 80ms |

### Endpoints
| Endpoint | Method | Description |
|----------|--------|-------------|
| /predict | POST | Get prediction |
| /health | GET | Health check |

---

## Monitoring

### Metrics to Track
- Prediction distribution
- Latency percentiles
- Error rate
- Data drift indicators

### Alerting Thresholds
| Metric | Warning | Critical |
|--------|---------|----------|
| Latency p99 | > 100ms | > 200ms |
| Error Rate | > 1% | > 5% |
| Drift Score | > 0.1 | > 0.2 |

### Retraining Triggers
- [Trigger 1]: [Condition]
- [Trigger 2]: [Condition]

---

## Version History

| Version | Date | Changes |
|---------|------|---------|
| 1.0.0 | YYYY-MM-DD | Initial release |
| 1.1.0 | YYYY-MM-DD | Added feature X |
| 2.0.0 | YYYY-MM-DD | Major architecture change |

---

## References

- [Link to training code]
- [Link to data documentation]
- [Link to related papers]
- [Link to API documentation]

---

## Contact

For questions or issues:
- Team: [Team name]
- Email: [Contact email]
- Slack: [Channel]
```

---

## Quick Checklist

### Before Release

- [ ] Model architecture documented
- [ ] Training data described
- [ ] Evaluation metrics included
- [ ] Fairness analysis completed
- [ ] Ethical risks assessed
- [ ] Deployment requirements listed
- [ ] Monitoring plan defined
- [ ] Version history updated

### Review Questions

1. Is the intended use clearly defined?
2. Are limitations and risks documented?
3. Can another team reproduce training?
4. Are bias metrics acceptable?
5. Is there a monitoring plan?