How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines

This article details how Amazon uses Amazon SageMaker Pipelines to train sequential ensemble models for use case identification in Salesforce opportunities, using a multi-layered BERTopic approach.

• The solution uses three sequential BERTopic models to generate hierarchical topic clustering
• Each BERTopic model consists of embedding, dimension reduction, clustering, and keyword identification steps
• Key challenges include data preprocessing, scalable compute, and coordinating multi-layer model training
• The implementation leverages SageMaker pipeline steps including Processing, Training, Callback, and Model registration
• Uses custom Docker images and integrates with AWS services like SQS and Lambda for workflow orchestration

The approach enables automatic identification of use cases from text data, improving sales analytics and recommendation models through advanced machine learning techniques.