Media — Content Recommendation Model Training Pipeline

Overview

The purpose of this DAG is to train machine learning models that predict user preferences based on streaming data, ultimately improving content recommendations in the media industry. The data sources include user interaction logs, streaming metadata, and historical viewing patterns, which are ingested into the pipeline for processing. The ingestion pipeline first cleans and preprocesses the data, ensuring it is suitable for model training. The data is then split into training and testing sets to

The purpose of this DAG is to train machine learning models that predict user preferences based on streaming data, ultimately improving content recommendations in the media industry. The data sources include user interaction logs, streaming metadata, and historical viewing patterns, which are ingested into the pipeline for processing. The ingestion pipeline first cleans and preprocesses the data, ensuring it is suitable for model training. The data is then split into training and testing sets to evaluate model performance accurately. During the processing steps, various machine learning algorithms are applied, and key performance metrics such as accuracy, precision, and recall are calculated to assess the effectiveness of the models. The trained models are subsequently stored in a model registry for future deployment in production environments. Monitoring KPIs, including model performance and user engagement metrics, are tracked to ensure ongoing optimization. The business value of this DAG lies in its ability to enhance user experience through personalized content recommendations, ultimately leading to increased viewer retention and satisfaction.

Part of the Predictive Maintenance solution for the Media industry.

Use cases

• Increased user engagement through personalized recommendations
• Enhanced viewer retention by predicting user preferences
• Data-driven insights for content strategy optimization
• Reduced churn rates by improving user satisfaction
• Scalable model training for evolving user behavior