Diagnosis on
online learning
performance
Identification of
concept drift
detection criteria
Identification of
concept drift
adaptation procedures
Customization of
feature and data store
management strategies
MOTIVATION
Performance drop after deployment is not an exception
Retraining takes time, money, and often guesswork
Retraining strategies rarely match real-world needs
Your models are running, but are they still learning?
MOTIVATION
Post-deployment performance drop is routine
Retraining takes time, money, and often guesswork
Retraining strategies rarely match real-world needs
Your models are running, but are they still learning?
SOLUTION
Tailors retraining strategies to your environment
Pinpoints the right time and right way to retrain
Modular architecture for seamless integration
Keeps your model sharp with autonomous improvement
Tailors retraining strategies to your environment
Pinpoints the right time and right way to retrain
Modular architecture for seamless integration
Keeps your model sharp with auto-tuning
DETECTION
Precisely detect the right moment to update
Accurately classify emerging data trends
Measure and interpret concept drifts with clarity
Enable intelligent and automated model updates
Precisely detect the right moment to update
Accurately classify emerging data trends
Measure and interpret concept drifts with clarity
Enable intelligent and automated model updates
ADAPTATION
Empower cumulative data through data store updates
Reinvent predictor structures with feature store updates
Elevate model performance with model store updates
Adaptive update strategies crafted for data evolution
Empower cumulative data through data store updates
Evolve predictors through feature store updates
Elevate model performance with model store updates
Adaptive update strategies crafted for data evolution
DIAGNOSIS
Run wide-scale tests to measure retraining impact
Simulate retraining scenarios for peak performance
Validate both feature replacement and model swap strategies
Build a strategy for storing, learning, and referencing data
Run wide-scale tests to measure retraining impact
Simulate retraining scenarios for peak performance
Validate feature updates and model swap strategies
Plan how to store, learn from, and reference data
TREATMENT
Plug into legacy systems via simple scheduling hooks
Integrate directly into your MLOps pipeline as a CI/CD module
Visualize it all through your existing dashboards
Sync with your feature and data stores for full-cycle control
Plug into legacy systems via simple scheduling hooks
Integrate with your MLOps pipeline as a CI/CD module
Visualize it all through your existing dashboards
Sync with your feature and data stores for full control
POSCO BPED Virtual Metrology Model, OPTIMIZED
Widely running retraining impact tests to optimize model performance
Nov 30, 2025
LG U+ Voice Phishing Detection Model, OPTIMIZED
Running wide-scale tests to measure retraining impacts
May 31, 2025
8 Auto-retraining Patents Issued in Q1 2025, PATENTED
Relentlessly advancing IP strategy to drive future growth
Apr 01, 2025
POSCO BPED Virtual Metrology Model, OPTIMIZED
Widely running retraining impact tests to optimize model performance
Nov 30, 2025
LG U+ Voice Phishing Detection Model, OPTIMIZED
Running wide-scale tests to measure retraining impacts
May 31, 2025
8 Auto-retraining Patents Issued in Q1 2025, PATENTED
Relentlessly advancing IP strategy to drive future growth
Apr 01, 2025