Retail — E-commerce Recommendations Model Training Pipeline

Overview

The purpose of this DAG is to manage the training of recommendation models tailored for the retail e-commerce sector, utilizing prepared customer features. The pipeline begins by ingesting relevant data sources, including customer transaction histories and product interactions. Once the data is ingested, it undergoes preprocessing steps to clean and transform the features, ensuring high-quality input for model training. The next phase involves model selection, where various algorithms are evalua

The purpose of this DAG is to manage the training of recommendation models tailored for the retail e-commerce sector, utilizing prepared customer features. The pipeline begins by ingesting relevant data sources, including customer transaction histories and product interactions. Once the data is ingested, it undergoes preprocessing steps to clean and transform the features, ensuring high-quality input for model training. The next phase involves model selection, where various algorithms are evaluated based on their performance metrics. After selecting the best-performing models, the DAG incorporates a thorough evaluation process to monitor for any biases or drifts in the model's predictions. The trained models are then deployed via a RESTful API, allowing real-time access to recommendations for users. Throughout this process, key performance indicators (KPIs) such as accuracy, precision, and recall are monitored to ensure the effectiveness of the recommendation system. The business value of this DAG lies in its ability to enhance customer engagement and drive sales through personalized recommendations, ultimately leading to improved customer satisfaction and retention.

Part of the Recommendations solution for the Retail industry.

Use cases

• Increases customer engagement through personalized recommendations
• Enhances sales by providing targeted product suggestions
• Improves customer satisfaction and loyalty with relevant offers
• Reduces operational costs by automating recommendation processes
• Facilitates data-driven decision-making for marketing strategies