diff --git a/work/tbx/Meraki_Dashboard_API_for_IoT_and_ML_Integrations.md b/work/tbx/Meraki_Dashboard_API_for_IoT_and_ML_Integrations.md
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--- a/work/tbx/Meraki_Dashboard_API_for_IoT_and_ML_Integrations.md
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@@ -1,3 +1,215 @@
+### High-Level Jenkins Pipeline Outline for Leveraging Meraki Dashboard API for IoT and ML Integrations
+
+This Jenkins pipeline will manage the entire workflow from data ingestion to model deployment and monitoring, ensuring a streamlined and automated process. Here's an outline of the complete pipeline:
+
+---
+
+### I. Setup Stage
+
+#### Purpose:
+- Prepare the environment for the subsequent stages.
+- Install necessary dependencies and tools.
+
+#### Steps:
+1. **Initialize Environment**
+   - Install Docker and Kubernetes CLI.
+   - Install Python and necessary libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
+   - Setup virtual environment for Python dependencies.
+
+2. **Install Dependencies**
+   - Install Meraki SDK and MQTT libraries.
+   - Install any other required dependencies listed in `requirements.txt`.
+
+#### Code:
+```groovy
+stage('Setup') {
+    steps {
+        script {
+            sh 'sudo apt-get update'
+            sh 'sudo apt-get install -y docker.io'
+            sh 'sudo apt-get install -y kubectl'
+            sh 'pip install virtualenv'
+            sh 'virtualenv venv'
+            sh '. venv/bin/activate'
+            sh 'pip install -r requirements.txt'
+        }
+    }
+}
+```
+
+### II. Data Ingestion Stage
+
+#### Purpose:
+- Fetch and store sensor data using the Meraki API and MQTT.
+
+#### Steps:
+1. **API Configuration**
+   - Configure API access and fetch data.
+   
+2. **MQTT Client Setup**
+   - Setup MQTT client to subscribe and ingest sensor data.
+
+3. **Database Connection**
+   - Connect to TimescaleDB and prepare for data storage.
+
+4. **Ingest Data**
+   - Store fetched data into the database.
+
+#### Code:
+```groovy
+stage('Data Ingestion') {
+    steps {
+        script {
+            sh 'python scripts/configure_api_access.py'
+            sh 'python scripts/setup_mqtt_client.py'
+            sh 'python scripts/connect_to_database.py'
+            sh 'python scripts/ingest_data.py'
+        }
+    }
+}
+```
+
+### III. Data Processing and Transformation Stage
+
+#### Purpose:
+- Normalize, segment, and integrate data for model training.
+
+#### Steps:
+1. **Normalize Data**
+   - Normalize the sensor data.
+   
+2. **Segment Data**
+   - Segment the data for model input.
+
+3. **Integrate Data**
+   - Merge data from multiple sensors into a comprehensive dataset.
+
+#### Code:
+```groovy
+stage('Data Processing and Transformation') {
+    steps {
+        script {
+            sh 'python scripts/normalize_data.py'
+            sh 'python scripts/segment_data.py'
+            sh 'python scripts/integrate_data.py'
+        }
+    }
+}
+```
+
+### IV. Model Development and Training Stage
+
+#### Purpose:
+- Define, train, and validate machine learning models.
+
+#### Steps:
+1. **Define Model Architecture**
+   - Set up the architecture for ML models including transformers and vision models.
+   
+2. **Train Models**
+   - Train the models using historical sensor data.
+
+3. **Validate Models**
+   - Validate and fine-tune model performance.
+
+#### Code:
+```groovy
+stage('Model Development and Training') {
+    steps {
+        script {
+            sh 'python scripts/define_model_architecture.py'
+            sh 'python scripts/train_model.py'
+            sh 'python scripts/validate_model.py'
+        }
+    }
+}
+```
+
+### V. Deployment and Integration Stage
+
+#### Purpose:
+- Deploy models using Docker and Kubernetes, and integrate them with real-time data streams.
+
+#### Steps:
+1. **Containerize Models**
+   - Package models into Docker containers.
+   
+2. **Deploy to Kubernetes**
+   - Deploy containers to a Kubernetes cluster.
+
+3. **Real-time Integration**
+   - Integrate models with real-time data streams for live predictions.
+
+#### Code:
+```groovy
+stage('Deployment and Integration') {
+    steps {
+        script {
+            sh 'docker build -t my_model_image .'
+            sh 'kubectl apply -f k8s/deployment.yaml'
+            sh 'python scripts/integrate_real_time_predictions.py'
+        }
+    }
+}
+```
+
+### VI. Visualization and Monitoring Stage
+
+#### Purpose:
+- Set up dashboards for data visualization and configure automated alerts.
+
+#### Steps:
+1. **Configure Dashboards**
+   - Setup Grafana dashboards for data visualization.
+   
+2. **Setup Alerts**
+   - Configure automated alerts for anomalies and critical events.
+
+3. **Monitor System**
+   - Continuously monitor system performance and sensor data.
+
+#### Code:
+```groovy
+stage('Visualization and Monitoring') {
+    steps {
+        script {
+            sh 'python scripts/configure_dashboard.py'
+            sh 'python scripts/setup_alerts.py'
+            sh 'python scripts/monitor_system.py'
+        }
+    }
+}
+```
+
+### VII. Cleanup Stage
+
+#### Purpose:
+- Clean up resources and temporary files to maintain the environment.
+
+#### Steps:
+1. **Cleanup**
+   - Remove temporary files and containers.
+   - Ensure the environment is reset for the next pipeline run.
+
+#### Code:
+```groovy
+stage('Cleanup') {
+    steps {
+        script {
+            sh 'docker system prune -f'
+            sh 'kubectl delete -f k8s/deployment.yaml'
+            sh 'rm -rf venv'
+        }
+    }
+}
+```
+
+### Summary
+
+This high-level Jenkins pipeline outline provides a comprehensive framework for automating the workflow of leveraging Meraki's API for IoT and ML integrations. Each stage addresses a specific part of the process, ensuring a cohesive and streamlined deployment from data ingestion to visualization and monitoring.
+
+---
+
 ### I. Data Ingestion Layer
 
 #### Function Responsibilities: