Telecom — Automated Recommendation Model Retraining Pipeline

Overview

The purpose of this DAG is to automate the retraining of recommendation models based on newly ingested data within the telecom sector. The process begins with the ingestion of various data sources, including customer interaction logs, usage statistics, and feedback forms. Once the data is collected, it undergoes an analysis phase to assess whether the existing models require retraining based on performance metrics. If retraining is deemed necessary, the models are evaluated against the latest da

The purpose of this DAG is to automate the retraining of recommendation models based on newly ingested data within the telecom sector. The process begins with the ingestion of various data sources, including customer interaction logs, usage statistics, and feedback forms. Once the data is collected, it undergoes an analysis phase to assess whether the existing models require retraining based on performance metrics. If retraining is deemed necessary, the models are evaluated against the latest data, and updates are implemented to improve their accuracy and relevance. Quality control measures, such as A/B testing and performance validation, are applied to ensure that the updated models meet predefined standards. The outputs of this DAG include updated recommendation models and performance reports detailing the improvements achieved. Monitoring is facilitated through key performance indicators (KPIs) such as recommendation accuracy, user engagement rates, and overall satisfaction scores. The business value of this automated retraining process lies in its ability to enhance customer experience, increase retention rates, and drive revenue growth by providing personalized recommendations that adapt to changing user behaviors.

Part of the Recommendations solution for the Telecom industry.

Use cases

• Increased accuracy of recommendations leading to higher customer satisfaction
• Reduced manual intervention in the model retraining process
• Enhanced ability to adapt to changing customer preferences
• Improved customer retention through personalized experiences
• Data-driven insights for strategic decision-making