diff --git a/projects/getting_started_ml.md b/projects/getting_started_ml.md index a4127f5..730ba72 100644 --- a/projects/getting_started_ml.md +++ b/projects/getting_started_ml.md @@ -871,4 +871,159 @@ plt.show() - **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. \ No newline at end of file +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. \ No newline at end of file