High Tech — Feature Pipeline for Predictive Maintenance Models

Overview

The purpose of this DAG is to create robust feature pipelines that support the training of predictive maintenance models in the high-tech industry. It ingests data from various sources, including historical equipment performance logs, real-time sensor data, and maintenance records. The ingestion pipeline is designed to handle both batch and streaming data, ensuring timely updates for model training. The processing steps include data transformation, where raw data is cleaned and normalized, follo

The purpose of this DAG is to create robust feature pipelines that support the training of predictive maintenance models in the high-tech industry. It ingests data from various sources, including historical equipment performance logs, real-time sensor data, and maintenance records. The ingestion pipeline is designed to handle both batch and streaming data, ensuring timely updates for model training. The processing steps include data transformation, where raw data is cleaned and normalized, followed by feature engineering to extract relevant attributes that enhance model performance. Quality control measures are implemented to validate data integrity, ensuring that only high-quality data contributes to model training. The outputs of this DAG include a set of engineered features ready for machine learning model training, performance evaluation metrics, and a report on data quality. Monitoring key performance indicators (KPIs) such as model accuracy, precision, and recall are essential for assessing the effectiveness of the predictive models. In case of failures during the pipeline execution, a recovery process is initiated to ensure continuous operation. The business value derived from this DAG includes improved predictive maintenance capabilities, reduced downtime, enhanced operational efficiency, and ultimately, cost savings for organizations in the high-tech sector.

Part of the Predictive Maintenance solution for the High Tech industry.

Use cases

• Enhanced model accuracy leading to better maintenance predictions
• Reduced equipment downtime through proactive maintenance strategies
• Cost savings from optimized maintenance schedules
• Improved operational efficiency across high-tech assets
• Data-driven decision-making supported by real-time insights