Models That Improve Themselves
Continuous Training and Continuous Deployment. Close the feedback loop, automate retraining, and deploy with confidence using shadow models.
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.
Capture predictions
Every inference logged automatically
Review anomalies
Flag and correct mispredictions
Enrich datasets
Add reviewed data to training sets
Trigger retraining
Automated based on thresholds
# 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
Retraining Triggers
ConfigureTrigger retraining when reviewed predictions reach threshold
Trigger when distribution drift exceeds threshold
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
Deploy shadow model
New model version runs in parallel, processing the same inputs as production
Compare predictions
Both models make predictions, but only primary results are returned to users
Promote when ready
Once shadow outperforms primary, promote it with a single command
Live Comparison
# 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)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.