Power BI Dashboard for ML Model Tracking
Budget: ₹600 – ₹1,500 INR
overview
A Power BI dashboard designed to monitor, compare, and analyze the performance of multiple machine learning model versions deployed in production or testing environments. It helps ML and Data Science teams ensure models continue to perform well, detect performance degradation, and support decision-making for model updates or retraining.
Key Features
1.Multi-Version Monitoring Track and visualize the performance of multiple model versions (e.g., v1.0, v1.1, v2.0) at the same time.
2.Real-time KPI Tracking KPIs like Accuracy, Precision, Recall, Prediction Volume, and Drift Score are aggregated and visualized clearly.
3.Model Performance Comparison Side-by-side charts help easily compare different model versions across core evaluation metrics.
4.Accuracy and Drift Over Time Trends are captured monthly to highlight when models start degrading, drifting, or require retraining.
5.Volume Analysis Understand how many predictions each model version made and whether newer versions are more actively used.
6.Interactive Filters Filters for model version and time periods allow focused analysis for specific investigations.
7.Drift Monitoring Integration of drift scores helps identify if the data distribution has changed, indicating when models may become less reliable
A Power BI dashboard designed to monitor, compare, and analyze the performance of multiple machine learning model versions deployed in production or testing environments. It helps ML and Data Science teams ensure models continue to perform well, detect performance degradation, and support decision-making for model updates or retraining.
Key Features
1.Multi-Version Monitoring Track and visualize the performance of multiple model versions (e.g., v1.0, v1.1, v2.0) at the same time.
2.Real-time KPI Tracking KPIs like Accuracy, Precision, Recall, Prediction Volume, and Drift Score are aggregated and visualized clearly.
3.Model Performance Comparison Side-by-side charts help easily compare different model versions across core evaluation metrics.
4.Accuracy and Drift Over Time Trends are captured monthly to highlight when models start degrading, drifting, or require retraining.
5.Volume Analysis Understand how many predictions each model version made and whether newer versions are more actively used.
6.Interactive Filters Filters for model version and time periods allow focused analysis for specific investigations.
7.Drift Monitoring Integration of drift scores helps identify if the data distribution has changed, indicating when models may become less reliable