Update projects/getting_started_ml.md

projects/getting_started_ml.md

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 - **Model Evaluation:** Evaluate the model’s performance using appropriate metrics.
 - **Advanced Techniques:** Use feature selection, ensemble methods, and cross-validation to improve the model.
 
-By following this structured approach, you can effectively select and train a baseline machine learning model to predict target variables using MQTT sensor data.
+By following this structured approach, you can effectively select and train a baseline machine learning model to predict target variables using MQTT sensor data.
+
+---
+
+### Model Inference: Functions Overview
+
+#### 1. Define the Use Case
+
+- **Objective:** Clearly state the goal of the prediction during inference.
+- **Real-time or Batch:** Determine if the inference will be performed in real-time or in batch mode.
+- **Expected Output:** Define the expected output of the inference process.
+
+#### 2. Data Collection for Inference
+
+- **Real-time Data Collection:** Collect real-time data via MQTT for immediate inference.
+- **Batch Data Collection:** Collect and aggregate data over a period for batch inference.
+
+#### 3. Data Preprocessing for Inference
+
+- **Handle Missing Values:** Replace missing values with appropriate substitutes.
+- **Feature Engineering:** Apply the same feature engineering steps used during training (e.g., lag features, rolling statistics).
+- **Normalization/Scaling:** Ensure the features are scaled consistently with the training data.
+
+#### 4. Load the Trained Model
+
+- **Model Serialization:** Load the trained model from storage (e.g., joblib, pickle).
+- **Environment Setup:** Ensure the inference environment matches the training environment.
+
+#### 5. Perform Inference
+
+- **Predict:** Use the trained model to make predictions on the preprocessed data.
+- **Post-process Results:** Convert the raw predictions into actionable insights.
+
+#### 6. Monitoring and Logging
+
+- **Log Predictions:** Store predictions for future analysis and auditing.
+- **Monitor Performance:** Track the performance of the model over time to detect drift.
+
+### Function Descriptions
+
+#### Data Collection for Inference
+
+1. **collect_real_time_data:**
+   - **Purpose:** Gather real-time data from MQTT sensors for immediate inference.
+   
+2. **collect_batch_data:**
+   - **Purpose:** Collect and aggregate sensor data over a specified period for batch inference.
+
+#### Data Preprocessing for Inference
+
+3. **preprocess_inference_data:**
+   - **Purpose:** Clean and preprocess the collected data, ensuring consistency with the training data preprocessing steps.
+
+4. **feature_engineering_inference:**
+   - **Purpose:** Apply feature engineering steps to the inference data (e.g., creating lag features, rolling statistics).
+
+5. **normalize_data:**
+   - **Purpose:** Normalize or scale the features to match the training data’s distribution.
+
+#### Load the Trained Model
+
+6. **load_trained_model:**
+   - **Purpose:** Load the trained machine learning model from storage.
+
+#### Perform Inference
+
+7. **perform_inference:**
+   - **Purpose:** Use the trained model to make predictions on the preprocessed inference data.
+
+8. **post_process_predictions:**
+   - **Purpose:** Convert raw model predictions into actionable insights or outputs.
+
+#### Monitoring and Logging
+
+9. **log_predictions:**
+   - **Purpose:** Log predictions for auditing and future analysis.
+
+10. **monitor_model_performance:**
+    - **Purpose:** Monitor the performance of the model over time to detect any degradation or drift.
+
+### Detailed Overview of Inference Functions
+
+#### Data Collection for Inference
+
+1. **Function: collect_real_time_data**
+   - **Purpose:** Gather real-time data from MQTT sensors for immediate inference.
+   - **Description:** Connects to the MQTT broker, subscribes to relevant topics, and collects incoming messages.
+
+2. **Function: collect_batch_data**
+   - **Purpose:** Collect and aggregate sensor data over a specified period for batch inference.
+   - **Description:** Queries the TimescaleDB to retrieve aggregated data for batch processing.
+
+#### Data Preprocessing for Inference
+
+3. **Function: preprocess_inference_data**
+   - **Purpose:** Clean and preprocess the collected data, ensuring consistency with the training data preprocessing steps.
+   - **Description:** Handles missing values and applies necessary transformations to prepare the data for inference.
+
+4. **Function: feature_engineering_inference**
+   - **Purpose:** Apply feature engineering steps to the inference data (e.g., creating lag features, rolling statistics).
+   - **Description:** Applies the same feature engineering techniques used during training to ensure consistency.
+
+5. **Function: normalize_data**
+   - **Purpose:** Normalize or scale the features to match the training data’s distribution.
+   - **Description:** Uses the same normalization/scaling parameters used during training.
+
+#### Load the Trained Model
+
+6. **Function: load_trained_model**
+   - **Purpose:** Load the trained machine learning model from storage.
+   - **Description:** Deserializes the model using joblib or pickle.
+
+#### Perform Inference
+
+7. **Function: perform_inference**
+   - **Purpose:** Use the trained model to make predictions on the preprocessed inference data.
+   - **Description:** Applies the model to the preprocessed data to generate predictions.
+
+8. **Function: post_process_predictions**
+   - **Purpose:** Convert raw model predictions into actionable insights or outputs.
+   - **Description:** Transforms the predictions into a user-friendly format or actionable output.
+
+#### Monitoring and Logging
+
+9. **Function: log_predictions**
+   - **Purpose:** Log predictions for auditing and future analysis.
+   - **Description:** Stores predictions in a database or log file for future reference.
+
+10. **Function: monitor_model_performance**
+    - **Purpose:** Monitor the performance of the model over time to detect any degradation or drift.
+    - **Description:** Tracks model performance metrics and alerts if performance degrades.
+
+### Example Workflow for Inference
+
+1. **Data Collection for Inference:**
+   - **Real-time:** `collect_real_time_data()`
+   - **Batch:** `collect_batch_data()`
+
+2. **Data Preprocessing for Inference:**
+   - `preprocess_inference_data()`
+   - `feature_engineering_inference()`
+   - `normalize_data()`
+
+3. **Load the Trained Model:**
+   - `load_trained_model()`
+
+4. **Perform Inference:**
+   - `perform_inference()`
+   - `post_process_predictions()`
+
+5. **Monitoring and Logging:**
+   - `log_predictions()`
+   - `monitor_model_performance()`
+
+### Summary
+By following this structured approach, you can effectively perform inference using sensor data collected via MQTT. Each function plays a critical role in ensuring the predictions are accurate, reliable, and actionable. This process ensures that the model’s performance is maintained and monitored over time, providing valuable insights and driving decision-making.