How Amazon optimizes their supply chain with help from AWS Batch

The article discusses how Amazon optimizes their supply chain by utilizing AWS Batch for running machine learning models that provide transportation network planning recommendations.

Specifically, the article covers:

• Amazon's team that provides optimal inventory distribution recommendations by running containerized ML models
• The previous architecture involving orchestrating EC2 instances using AWS Step Functions, and the challenges faced with scaling
• The new architecture using AWS Batch, which allowed for better scalability, higher quality of service, and reduced complexity
• Performance improvements achieved after migrating to AWS Batch, handling higher concurrency of model executions
• Lessons learned, including designing for scale from the start, planning resource allocation, keeping instance types simple, and considering risks of running multiple jobs on the same instance
• Future plans to use Fargate compute and job queue prioritization with AWS Batch