Telecom — Predictive Maintenance Model Training Pipeline

Overview

The purpose of this DAG is to develop machine learning models that predict potential failures in critical telecom equipment, thereby enabling proactive maintenance strategies. The data sources include equipment health metrics, historical failure logs, and environmental factors that influence equipment performance. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and normalization to ensure high-quality inputs for model training. The processing ste

The purpose of this DAG is to develop machine learning models that predict potential failures in critical telecom equipment, thereby enabling proactive maintenance strategies. The data sources include equipment health metrics, historical failure logs, and environmental factors that influence equipment performance. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and normalization to ensure high-quality inputs for model training. The processing steps involve feature selection, where relevant attributes are identified, and then various machine learning algorithms are applied to train the models. Performance metrics such as accuracy, precision, recall, and F1 score are monitored throughout the training process to evaluate model effectiveness. In case of model training failures, a robust recovery mechanism is implemented to restart the training process without data loss. The outputs of this DAG include the best-performing predictive models, a performance report, and a model deployment package. Key performance indicators (KPIs) such as model accuracy and training time are tracked to ensure continuous improvement. The business value of this DAG lies in its ability to minimize unexpected equipment failures, optimize maintenance schedules, and ultimately enhance service reliability and customer satisfaction in the telecom industry.

Part of the Predictive Maintenance solution for the Telecom industry.

Use cases

• Reduces unexpected equipment failures and downtime
• Enhances efficiency of maintenance operations
• Improves customer satisfaction through reliable services
• Optimizes resource allocation for maintenance teams
• Facilitates data-driven decision-making in operations