Shadcn.io is not affiliated with official shadcn/ui
Monitoring Machine Learning Model Drift
Advanced ML monitoring dashboard featuring feature drift tracking, label distribution shifts, and model performance decay with shadcn/ui and Recharts.
Maintain the accuracy of your AI features with this ML model drift block. It provides real-time visibility into feature distribution shifts and prediction decay, helping you identify when models need retraining or data distributions have fundamentally changed. Built for MLOps teams, it bridges the gap between model deployment and long-term performance stability.
Related Components
Inference Latency
ML model response tracking
GPU Utilization
Hardware performance monitor
Vector DB Latency
Retrieval speed tracking
OpenAI Usage
LLM cost & token tracking
Pipeline Freshness
Data latency monitoring
Server Health
Infrastructure vital signs
FAQ
Was this page helpful?
Sign in to leave feedback.
Log Stream
Real-time log visualization block featuring live tailing, level-based filtering, and auto-scrolling terminal interface for application observability.
Memory Usage
Comprehensive memory monitoring block featuring stacked area charts for heap and resident set size (RSS), memory leak detection, and cache efficiency metrics with Recharts and shadcn/ui.