Classification
Customer Churn Prediction
Binary classification model for identifying at-risk customers and supporting retention action planning.
metricsfeature_importancerocconfusion_matrix
ML Observability
Model metrics & insights
Centralized dashboard for inspecting ML model metrics, artifacts, and interpretation signals. Artifact-driven and model-agnostic, so new projects can register their outputs and become observable without rebuilding the interface layer.
3Integrated ML systems
ArtifactsMetrics, importance, ROC, confusion matrix, metadata
Registry-basedModel-agnostic observability layer
Models
Registered model systems
Each model card exposes task type, available artifacts, and the role it plays in the portfolio. The observability layer is designed so more systems can be added later with the same interface pattern.
Forecasting
Inventory Demand Forecasting
Next-day demand prediction system for store-item planning and inventory control workflows.
metricsfeature_importanceforecast_seriesmetadata
Classification
Credit Risk Prediction
Default risk scoring system for lending decisions, risk bands, and explainable screening.
metricsfeature_importancerocconfusion_matrix
Overview
Evaluation metrics
This example dashboard view is centered on the churn model. It surfaces the most important quality signals first, then connects them to interpretability and model metadata.
0.8725ROC-AUC
0.7003Precision
0.5561Recall
0.6200F1 Score
Observability stack
How this layer is structured
The page acts like a registry-driven dashboard that reads artifacts and renders them consistently across multiple ML projects.
Model registry provides available systems and artifact locations.
Metrics and plot-ready data are loaded from saved JSON or serialized outputs.
UI components stay stable even when the underlying model or task changes.
New projects can integrate by publishing compatible artifacts to the registry.
Metrics
Model performance snapshot
These evaluation metrics summarize the churn model's classification quality and make it easy to compare tradeoffs across systems.
0.8725roc_auc
0.7003precision
0.5561recall
0.6200f1
Feature importance
Top drivers for churn prediction
The most influential features show which customer attributes are most associated with churn behavior in this model.
Contract
4.0%
Senior Citizen
1.7%
Tenure in Months
1.6%
Dependents
1.5%
is_month_to_month
1.2%
Payment Method
0.9%
Monthly Charge
0.7%
service_count
-0.6%
Unlimited Data
-0.5%
Premium Tech Support
-0.5%
Internet Service
-0.4%
tenure_group
-0.4%
Visual analysis
ROC, confusion matrix, and metadata
This side of the dashboard combines threshold-independent quality, threshold-specific outcomes, and model registry details in one operator-facing view.
ROC curve
FPR vs TPR1.00.80.60.40.20.0
0.00.20.40.60.81.0
Model ROC
Random baseline
946TN
89FP
166FN
208TP
Contract type is the strongest signal shaping churn decisions in this model.
Tenure and month-to-month behavior reinforce the importance of lifecycle-related risk.
The registry metadata allows the observability layer to stay generic while still showing model-specific details.