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.

# 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

Shadow outperforming on 3/4 metrics

SHADOW DEPLOYMENT SDK

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

# Run shadow prediction
prediction.predict_shadow()

# 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.