# Automatisation CT/CD

## Des modèles qui s'améliorent tout seuls

Entraînement continu et déploiement continu. Fermez la boucle de feedback, automatisez le ré-entraînement et déployez en toute confiance avec les modèles shadow.

0

Manual intervention  
24/7  
Automated loop  
100%  
Safe deployments  
∞  
Model iterations

## Production data becomes training data

Connect your deployment to your training pipeline. Reviewed predictions automatically flow into your training datasets, creating a continuous improvement cycle.

1

#### Capture predictions

Every inference logged automatically

2

#### Review anomalies

Flag and correct mispredictions

3

#### Enrich datasets

Add reviewed data to training sets

4

#### Trigger retraining

Automated based on thresholds

### Feedback Pipeline

Active

Predictions logged 12,847  
Reviewed 2,156  
Added to training set 1,892  
Ready to trigger  
1,892 / 2,000 threshold reached

### Retraining Triggers

Configure

Review Threshold  
Active  
Trigger retraining when reviewed predictions reach threshold  
750 / 1000

Drift Alert  
Monitoring  
Trigger when distribution drift exceeds threshold  
8% / 15%

Scheduled  
Disabled

Retrain on a fixed schedule (weekly, monthly)

## Automated retraining on your terms

Define triggers based on review thresholds, drift detection, or schedules. When conditions are met, Picsellia automatically provisions GPUs and launches training.

```python
# Setup continuous training
deployment.setup_continuous_training(
    trigger_threshold=1000,
    experiment_name="auto-retrain-v{n}",
    gpu_type="A10G"
)
```

## Deploy with confidence

Test new models in production without risk. Shadow models run alongside your primary model, comparing performance on real traffic before you promote.

### How Shadow Deployment Works

1

#### Deploy shadow model

New model version runs in parallel, processing the same inputs as production

2

#### Compare predictions

Both models make predictions, but only primary results are returned to users

3

#### Promote when ready

Once shadow outperforms primary, promote it with a single command

### Live Comparison

Primary v2.1  
Shadow v2.2

| Metric     | Primary | Shadow |
|------------|---------|--------|
| mAP        | 84.7%   | 89.2%  |
| Precision  | 91.0%   | 94.0%  |
| Recall     | 88.0%   | 86.0%  |
| Latency    | 45ms    | 52ms   |

Shadow outperforming on 3/4 metrics

### SHADOW DEPLOYMENT SDK

```python
# Deploy shadow model
deployment.set_shadow_model(
    model_version=new_version
)

# Run shadow prediction
prediction.predict_shadow()
```

```python
# Setup auto-promotion policy
department.setup_continuous_deployment(
    promotion_threshold=0.05,
    min_samples=1000
)

department.toggle_continuous_deployment(True)
```

## The complete automation stack

From data collection to model deployment, every step is connected and automated. Full lineage, full visibility.

### Continuous Training

Automatic retraining triggered by review thresholds or drift detection

### Continuous Deployment

Safe model promotion with shadow deployment and automated policies

### Full Observability

Monitor every prediction, track every metric, trace every decision

## Ready to automate your ML lifecycle?

Set up continuous training and deployment in minutes. Let your models improve themselves.