Project Idea: Automated Model Performance Monitoring and Visualization

[PRIYANSHU2026]

Project Idea: Automated Model Performance Monitoring and Visualization

Project Description:
Develop a feature within Fabrik to automatically monitor and visualize the performance of deep learning models over time. This feature will allow users to track key performance metrics, visualize training progress, and receive alerts when models degrade or improve significantly. The monitoring system will support integration with external datasets for continuous validation.

Key Features:

1. Performance Dashboard: Create a dashboard within Fabrik that displays key performance metrics (e.g., accuracy, loss, precision, recall) for each model.
2. Training Progress Visualization: Visualize the training progress of models using graphs and charts, showing metrics like training loss, validation loss, and accuracy over epochs.
3. Continuous Validation: Integrate with external datasets to continuously validate the model's performance on new data, ensuring it remains accurate and relevant.
4. Automated Alerts: Set up automated alerts to notify users when there are significant changes in model performance, such as a drop in accuracy or an increase in loss.
5. Comparison Tool: Allow users to compare the performance of different models side-by-side, helping them choose the best model for their needs.
6. Historical Data Analysis: Store historical performance data to allow users to analyze trends and identify patterns over time.

Explanation:

1. Performance Metrics Storage: Define a Django model for storing performance metrics like accuracy and loss for each epoch.
2. Training Progress Plotting: Create a function to plot training accuracy and loss over epochs using Matplotlib.
3. Performance Dashboard: Develop a view and template to display the performance dashboard, including the training progress plots.

Useful Links:

• Fabrik GitHub Repository
• Django ORM Documentation
• Matplotlib Documentation

This project will enhance Fabrik by providing users with insights into their model's performance over time, helping them make informed decisions and improve their models iteratively.