An ecommerce company wants to build and train machine learning (ML) models to visualize complex scenarios and detect trends in customer data. The architecture team must integrate the ML models with a reporting platform so the augmented data can be analyzed and used directly in business intelligence dashboards. Which solution meets these requirements with the LEAST operational overhead?

Amazon SageMaker is a fully managed service designed for building, training, and deploying machine learning models at scale, making it ideal for the company's ML requirements with minimal operational overhead. Amazon QuickSight is a scalable, serverless business intelligence service that can directly ingest augmented data from SageMaker and create interactive dashboards for analysis, fulfilling the visualization and reporting needs. Incorrect options: AWS Glue ML transforms are primarily for data preparation and cleaning, not for building and training complex ML models for trend detection. Amazon OpenSearch Service is good for visualization but less suited for direct integration with complex ML model outputs for business intelligence dashboards compared to QuickSight. Using a pre-built ML AMI from the AWS Marketplace requires managing EC2 instances, which increases operational overhead compared to SageMaker's fully managed approach. Amazon QuickSight's calculated fields offer basic data manipulation but are not designed for building and training sophisticated machine learning models to detect complex trends.