If you are building multimodal artificial intelligence and machine learning (AI/ML) pipelines on Amazon Elastic Compute Cloud (Amazon EC2) that process text, video, and audio, you have likely hit at least one of these challenges. Scaling from a single instance to distributed compute without rewriting code, managing costs across training, fine-tuning, and inference, and keeping up with an evolving network of models, frameworks, and hardware are among the biggest challenges AI engineers face today. These challenges compound when each pipeline stage has distinct compute, networking, and storage requirements, and multiple teams own separate parts of the workflow.
This post shows how the Ray-powered Anyscale application on AWS helps you tackle these challenges. You will learn how to: deploy Ray across three AWS compute options based on your operational requirements, optimize costs by matching heterogeneous instance types to each pipeline stage, and scale from prototype to…