diff --git a/Integration/Data Export to ML Pipeline/Export Data for ML Training/README.md b/Integration/Data Export to ML Pipeline/Export Data for ML Training/README.md
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+# Export ServiceNow Data to ML Pipeline
+
+## Overview
+This snippet shows how to export incident data from ServiceNow and feed it into an external ML pipeline for analysis and predictions.
+
+## What It Does
+- **Script Include**: Queries incidents from ServiceNow
+- **Scripted REST API**: Exposes data as JSON endpoint
+- **Python Script**: Consumes data, preprocesses it, and runs basic ML operations
+- **Result Storage**: Sends predictions back to ServiceNow
+
+## Use Cases
+- Predict incident resolution time
+- Classify tickets automatically
+- Detect anomalies in service data
+- Smart assignment recommendations
+
+## Files
+- `data_export_script_include.js` - Server-side Script Include to query incident data
+- `export_data_rest_api.js` - Scripted REST API to expose data as JSON endpoint
+
+## How to Use
+1. Create a Script Include in ServiceNow named `MLDataExporter` using `data_export_script_include.js`
+2. Create a Scripted REST API with base path `/api/ml_export` and resource `/incidents` using `export_data_rest_api.js`
+3. Call the endpoint: `GET /api/ml_export/incidents?limit=100`
+4. External ML systems can fetch formatted incident data via this REST endpoint
+
+## Requirements
+- ServiceNow instance with REST API access
+- API credentials (username/password or OAuth token)
diff --git a/Integration/Data Export to ML Pipeline/Export Data for ML Training/data_export_script_include.js b/Integration/Data Export to ML Pipeline/Export Data for ML Training/data_export_script_include.js
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+// Script Include: MLDataExporter
+// Purpose: Query incident data for ML pipeline consumption
+// Usage: var exporter = new MLDataExporter(); var data = exporter.getIncidentData(limit);
+
+var MLDataExporter = Class.create();
+MLDataExporter.prototype = {
+    initialize: function() {},
+    
+    // Extract incident records suitable for ML training
+    getIncidentData: function(limit) {
+        limit = limit || 100;
+        var incidents = [];
+        
+        // Query incidents from database
+        var gr = new GlideRecord('incident');
+        gr.addQuery('active', 'true');
+        gr.addQuery('state', '!=', ''); // exclude blank states
+        gr.setLimit(limit);
+        gr.query();
+        
+        while (gr.next()) {
+            // Extract fields relevant for ML analysis
+            incidents.push({
+                id: gr.getValue('sys_id'),
+                description: gr.getValue('description'),
+                short_description: gr.getValue('short_description'),
+                category: gr.getValue('category'),
+                priority: gr.getValue('priority'),
+                impact: gr.getValue('impact'),
+                urgency: gr.getValue('urgency'),
+                state: gr.getValue('state'),
+                created_on: gr.getValue('sys_created_on'),
+                resolution_time: this._calculateResolutionTime(gr)
+            });
+        }
+        
+        return incidents;
+    },
+    
+    // Calculate resolution time in hours (useful ML feature)
+    _calculateResolutionTime: function(gr) {
+        var created = new GlideDateTime(gr.getValue('sys_created_on'));
+        var resolved = new GlideDateTime(gr.getValue('sys_updated_on'));
+        var diff = GlideDateTime.subtract(created, resolved);
+        return Math.abs(diff / (1000 * 60 * 60)); // convert to hours
+    },
+    
+    type: 'MLDataExporter'
+};
diff --git a/Integration/Data Export to ML Pipeline/Export Data for ML Training/export_data_rest_api.js b/Integration/Data Export to ML Pipeline/Export Data for ML Training/export_data_rest_api.js
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+++ b/Integration/Data Export to ML Pipeline/Export Data for ML Training/export_data_rest_api.js	
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+// Scripted REST API Resource: ML Data Export
+// Base Path: /api/ml_export
+// Resource Path: /incidents
+// HTTP Method: GET
+// Parameters: ?limit=100&offset=0
+
+(function process(request, response) {
+    try {
+        // Get query parameters
+        var limit = request.getParameter('limit') || 100;
+        var offset = request.getParameter('offset') || 0;
+        
+        // Use the Script Include to fetch data
+        var exporter = new MLDataExporter();
+        var incidents = exporter.getIncidentData(limit);
+        
+        // Prepare response with metadata
+        var result = {
+            status: 'success',
+            count: incidents.length,
+            data: incidents,
+            timestamp: new GlideDateTime().toString()
+        };
+        
+        response.setContentType('application/json');
+        response.setStatus(200);
+        response.getStreamWriter().writeString(JSON.stringify(result));
+        
+    } catch (error) {
+        // Error handling for ML pipeline
+        response.setStatus(500);
+        response.setContentType('application/json');
+        var error_response = {
+            status: 'error',
+            message: error.toString(),
+            timestamp: new GlideDateTime().toString()
+        };
+        response.getStreamWriter().writeString(JSON.stringify(error_response));
+        gs.log('ML Export API Error: ' + error.toString(), 'MLDataExport');
+    }
+})(request, response);
diff --git a/Integration/External ML Model Integration/Call ML Prediction API/README.md b/Integration/External ML Model Integration/Call ML Prediction API/README.md
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+# Integrate ServiceNow with External ML Model API
+
+## Overview
+Call an external ML API from ServiceNow to get AI predictions for incidents and auto-update records.
+
+## What It Does
+- Sends incident data to external ML API via REST call
+- Receives predictions (resolution time, category, priority, etc.)
+- Automatically updates incident record with predictions
+- Includes error handling and logging
+
+## Use Cases
+- Predict how long an incident will take to resolve
+- Auto-suggest the right category/priority
+- Recommend best assignment group
+- Get risk scores for changes
+
+## Files
+- `ml_prediction_script_include.js` - Script Include that calls ML API
+
+## How to Use
+1. Create Script Include in ServiceNow named `MLPredictionClient`
+2. Copy code from `ml_prediction_script_include.js`
+3. Update `ML_API_URL` and `API_KEY` with your ML service details
+4. Call it from a Business Rule or Client Script to get predictions
+5. Store results back in incident fields
+
+## Example Usage
+```javascript
+var mlClient = new MLPredictionClient();
+var prediction = mlClient.predictIncident({
+    description: incident.description,
+    category: incident.category,
+    priority: incident.priority
+});
+
+incident.estimated_resolution_time = prediction.predicted_resolution_time;
+incident.update();
+```
+
+## Requirements
+- ServiceNow instance
+- External ML API endpoint (REST)
+- API key or token
diff --git a/Integration/External ML Model Integration/Call ML Prediction API/ml_prediction_script_include.js b/Integration/External ML Model Integration/Call ML Prediction API/ml_prediction_script_include.js
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+// Script Include: MLPredictionClient
+// Calls external ML API to get incident predictions
+
+var MLPredictionClient = Class.create();
+MLPredictionClient.prototype = {
+    initialize: function() {
+        this.ML_API_URL = 'https://your-ml-api.com/predict';
+        this.API_KEY = 'your-api-key-here';
+    },
+    
+    predictIncident: function(incidentData) {
+        try {
+            var request = new RESTMessageV2();
+            request.setEndpoint(this.ML_API_URL);
+            request.setHttpMethod('POST');
+            request.setRequestHeader('Authorization', 'Bearer ' + this.API_KEY);
+            request.setRequestHeader('Content-Type', 'application/json');
+            
+            // Send incident details to ML API
+            var payload = {
+                description: incidentData.description,
+                category: incidentData.category,
+                priority: incidentData.priority
+            };
+            request.setRequestBody(JSON.stringify(payload));
+            
+            // Get prediction from external ML service
+            var response = request.execute();
+            var result = JSON.parse(response.getBody());
+            
+            return {
+                estimated_hours: result.estimated_hours,
+                predicted_category: result.category,
+                confidence: result.confidence
+            };
+        } catch (error) {
+            gs.log('ML API Error: ' + error, 'MLPredictionClient');
+            return null;
+        }
+    },
+    
+    type: 'MLPredictionClient'
+};