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ai_monitoring_dashboard [2025/05/28 14:51] – [Usage Examples] eagleeyenebulaai_monitoring_dashboard [2025/05/28 14:59] (current) – [Conclusion] eagleeyenebula
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 Enhance the dashboard to display alerts when metrics cross critical thresholds. Enhance the dashboard to display alerts when metrics cross critical thresholds.
  
-```python+<code> 
 +python
 import streamlit as st import streamlit as st
- +</code> 
-# Example performance data+**Example performance data** 
 +<code>
 performance_over_time = [0.89, 0.91, 0.93, 0.70, 0.67]  # Simulates a drop in accuracy performance_over_time = [0.89, 0.91, 0.93, 0.70, 0.67]  # Simulates a drop in accuracy
 logs_output = "INFO - Monitoring started\nERROR - Accuracy dropped below 75%." logs_output = "INFO - Monitoring started\nERROR - Accuracy dropped below 75%."
- +</code> 
-# Render dashboard+**Render dashboard** 
 +<code>
 st.title("AI Monitoring Dashboard") st.title("AI Monitoring Dashboard")
  
 st.header("Model Performance Over Time") st.header("Model Performance Over Time")
 st.line_chart(performance_over_time) st.line_chart(performance_over_time)
- +</code> 
-# Threshold alerts+**Threshold alerts** 
 +<code>
 if min(performance_over_time) < 0.75: if min(performance_over_time) < 0.75:
     st.error("ALERT: Model accuracy dropped below 75%!")     st.error("ALERT: Model accuracy dropped below 75%!")
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 st.header("System Logs") st.header("System Logs")
 st.text_area("Logs", logs_output, height=150) st.text_area("Logs", logs_output, height=150)
-```+</code>
  
 **Enhancements**: **Enhancements**:
-- Uses `st.error()` to issue prominent alerts about performance drops.   +   * Uses **st.error()** to issue prominent alerts about performance drops.   
-- Displays logs in a `st.text_area` widget for improved readability. +   * Displays logs in a **st.text_area** widget for improved readability.
- +
---- +
 ==== Example 2: Adding Interactive Filters ==== ==== Example 2: Adding Interactive Filters ====
  
 Allow users to filter and visualize specific metrics or data. Allow users to filter and visualize specific metrics or data.
  
-```python+<code> 
 +python
 import streamlit as st import streamlit as st
- +</code> 
-# Simulated metrics data+**Simulated metrics data** 
 +<code>
 metric_names = ["accuracy", "precision", "recall"] metric_names = ["accuracy", "precision", "recall"]
 data = { data = {
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     "recall": [0.86, 0.88, 0.91, 0.72],     "recall": [0.86, 0.88, 0.91, 0.72],
 } }
- +</code> 
-# Dashboard with dropdown filter+**Dashboard with dropdown filter** 
 +<code>
 st.title("AI Monitoring Dashboard") st.title("AI Monitoring Dashboard")
 selected_metric = st.selectbox("Select Metric to View:", metric_names) selected_metric = st.selectbox("Select Metric to View:", metric_names)
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 st.header(f"Performance: {selected_metric.title()}") st.header(f"Performance: {selected_metric.title()}")
 st.line_chart(data[selected_metric]) st.line_chart(data[selected_metric])
-```+</code>
  
 **Enhancements**: **Enhancements**:
-- Adds a `st.selectbox()` widget to dynamically filter and view specific metrics.   +   * Adds a **st.selectbox()** widget to dynamically filter and view specific metrics.   
-- Improves user interaction and customizability. +   * Improves user interaction and customizability.
- +
---- +
 ==== Example 3: Integrating with Backend Monitoring System ==== ==== Example 3: Integrating with Backend Monitoring System ====
  
-Fetch model metrics and logs directly from a backend monitoring component (e.g., `ModelMonitoring`).+Fetch model metrics and logs directly from a backend monitoring component (e.g., **ModelMonitoring**).
  
-```python+<code> 
 +python
 from ai_monitoring import ModelMonitoring from ai_monitoring import ModelMonitoring
 import streamlit as st import streamlit as st
- +</code> 
-# Simulated backend integration+**Simulated backend integration** 
 +<code>
 backend_monitor = ModelMonitoring() backend_monitor = ModelMonitoring()
- +</code> 
-# Simulate fetching data from model monitoring+**Simulate fetching data from model monitoring** 
 +<code>
 actuals = ["yes", "no", "yes", "yes", "no"] actuals = ["yes", "no", "yes", "yes", "no"]
 predictions = ["yes", "no", "no", "yes", "yes"] predictions = ["yes", "no", "no", "yes", "yes"]
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 metrics = backend_monitor.monitor_metrics(actuals, predictions) metrics = backend_monitor.monitor_metrics(actuals, predictions)
 logs_output = "INFO - Monitoring initialized\n" + "\n".join([f"{k}: {v}" for k, v in metrics.items()]) logs_output = "INFO - Monitoring initialized\n" + "\n".join([f"{k}: {v}" for k, v in metrics.items()])
- +</code> 
-# Dashboard rendering+**Dashboard rendering** 
 +<code>
 st.title("AI Monitoring Dashboard") st.title("AI Monitoring Dashboard")
  
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 st.header("System Logs") st.header("System Logs")
 st.text(logs_output) st.text(logs_output)
-```+</code>
  
 **Enhancements**: **Enhancements**:
-- Integrates the backend `ModelMonitoring` component for dynamic metric computation.   +   * Integrates the backend **ModelMonitoring** component for dynamic metric computation.   
-- Displays metrics in a structured JSON format using `st.json()`. +   * Displays metrics in a structured JSON format using **st.json()**.
- +
---- +
 ==== Example 4: Adding Real-Time Updates with Streamlit Widgets ==== ==== Example 4: Adding Real-Time Updates with Streamlit Widgets ====
  
 Enable dashboards to auto-refresh or display dynamic data over time. Enable dashboards to auto-refresh or display dynamic data over time.
- +<code> 
-```python+python
 import streamlit as st import streamlit as st
 import time import time
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 st.title("Real-Time AI Monitoring Dashboard") st.title("Real-Time AI Monitoring Dashboard")
 st.header("Dynamic Model Performance") st.header("Dynamic Model Performance")
- +</code> 
-# Simulated updating performance+**Simulated updating performance** 
 +<code>
 performance_over_time = [] performance_over_time = []
- +</code> 
-# Automatically update slider+**Automatically update slider** 
 +<code>
 times = st.slider("Number of updates to stream:", 1, 50, 5) times = st.slider("Number of updates to stream:", 1, 50, 5)
  
 st.write("Performance Chart (Live Updates)") st.write("Performance Chart (Live Updates)")
 chart = st.empty() chart = st.empty()
- +</code> 
-# Simulate live updates+**Simulate live updates** 
 +<code>
 for _ in range(times): for _ in range(times):
     point = random.uniform(0.80, 0.95)     point = random.uniform(0.80, 0.95)
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     chart.line_chart(performance_over_time)     chart.line_chart(performance_over_time)
     time.sleep(1)     time.sleep(1)
-```+</code>
  
 **Enhancements**: **Enhancements**:
-- Uses Streamlit’s `st.empty()` and a `time.sleep()` loop to create real-time data updates.   +   * Uses Streamlit’s **st.empty()** and a **time.sleep()** loop to create real-time data updates.   
-- Simulates how live monitoring dashboards look in production. +   * Simulates how live monitoring dashboards look in production.
- +
---- +
 ===== Extensibility ===== ===== Extensibility =====
  
 1. **Integrate Databases**:   1. **Integrate Databases**:  
-   Pull data from MongoDB (or other databases) to fetch model metrics or logs dynamically.+   * Pull data from MongoDB (or other databases) to fetch model metrics or logs dynamically.
  
 2. **Support Graph Dashboards**:   2. **Support Graph Dashboards**:  
-   Add advanced visualizations like bar charts, scatter plots, or heatmaps for deeper insights.+   * Add advanced visualizations like bar charts, scatter plots, or heatmaps for deeper insights.
  
 3. **Model Drift Analysis**:   3. **Model Drift Analysis**:  
-   Include dashboards for detecting and diagnosing model drift using metrics such as data distribution.+   * Include dashboards for detecting and diagnosing model drift using metrics such as data distribution.
  
 4. **Alerts via Notifications**:   4. **Alerts via Notifications**:  
-   Integrate Slack or email alerts to notify users about critical issues in real-time.+   * Integrate Slack or email alerts to notify users about critical issues in real-time.
  
 5. **Embed REST APIs**:   5. **Embed REST APIs**:  
-   Fetch external system data (e.g., cloud-based logs) by calling APIs dynamically. +   * Fetch external system data (e.g., cloud-based logs) by calling APIs dynamically.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
-- **Optimize for Real-Time Data**:   +* **Optimize for Real-Time Data**:   
-  Use efficient data-fetching strategies (e.g., caching data or limiting polling frequency).+  * Use efficient data-fetching strategies (e.g., caching data or limiting polling frequency).
  
-- **Ensure Responsive Design**:   +* **Ensure Responsive Design**:   
-  Keep the dashboard lightweight by avoiding overloading it with complex computations.+  * Keep the dashboard lightweight by avoiding overloading it with complex computations.
  
-- **Secure Sensitive Logs**:   +* **Secure Sensitive Logs**:   
-  Hide or encrypt sensitive system logs to ensure security compliance.+  * Hide or encrypt sensitive system logs to ensure security compliance.
  
-- **Integrate User Feedback**:   +* **Integrate User Feedback**:   
-  Allow users to comment or provide feedback directly through the interface for iterative improvement.+  * Allow users to comment or provide feedback directly through the interface for iterative improvement.
  
-- **Test with Mock Data**:   +* **Test with Mock Data**:   
-  Simulate usage with mock performance data and logs to validate setup before deployment.+  * Simulate usage with mock performance data and logs to validate setup before deployment. 
 +===== Conclusion =====
  
---- +The **AI Monitoring Dashboard** built with **Streamlit** offers a flexible and powerful framework for monitoring the health and performance of AI systems. Its simplicity, extensive visualization capability, and extensibility make it a reliable solution for production-grade model monitoring. By presenting key metrics such as accuracy trends, system resource usage, inference latency, and real-time logs in an intuitive UI, the dashboard provides teams with actionable insights that support fast diagnosis and continuous performance tuning.
- +
-===== Conclusion =====+
  
-The **AI Monitoring Dashboard** built with Streamlit offers a flexible and powerful framework for monitoring the health and performance of AI systems. Its simplicity, extensive visualization capability, and extensibility make it a reliable solution for production-grade model monitoring. Developers can further enhance the dashboard by integrating backend monitoring systems, APIs, and drift detection algorithms.+Beyond its default configuration, the dashboard serves as a foundation for building advanced observability features tailored to specific use cases. Developers can enhance functionality by integrating backend monitoring systems like Prometheus or ELK Stack, embedding **RESTful APIs** for external data sources, and implementing drift detection algorithms to catch behavioral shifts in deployed models. This adaptability ensures that the AI Monitoring Dashboard remains a scalable, future-proof asset in any **MLOps** pipeline, helping organizations maintain control, transparency, and trust in their AI systems.
ai_monitoring_dashboard.1748443864.txt.gz · Last modified: 2025/05/28 14:51 by eagleeyenebula