CT/CD Automation

Models That Improve Themselves

Continuous Training and Continuous Deployment. Close the feedback loop, automate retraining, and deploy with confidence using shadow models.

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

SDK

# Setup feedback loop
deployment.setup_feedback_loop()

# Attach dataset for enrichment
deployment.attach_dataset_version_to_feedback_loop(
  dataset_version=training_set
)

# Activate
deployment.toggle_feedback_loop(True)

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)

Training Orchestration

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.

• Review threshold triggers
• Distribution drift detection
• Scheduled retraining
• Automatic GPU provisioning
• Full experiment tracking

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

Shadow Deployment

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

• 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
deployment.setup_continuous_deployment(
  promotion_threshold=0.05,
  min_samples=1000
)

deployment.toggle_continuous_deployment(True)

End-to-End

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
  • Review-based triggers
  • Drift detection
  • GPU auto-provisioning
• Continuous Deployment
  • Safe model promotion with shadow deployment and automated policies
  • Shadow model testing
  • A/B comparison
  • One-click promotion
• Full Observability
  • Monitor every prediction, track every metric, trace every decision
  • Real-time dashboards
  • Anomaly alerts
  • Complete lineage

Ready to automate your ML lifecycle?

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