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AWS Solutions Library

Guidance for Low-Latency High-Throughput Model Inference Using Amazon ECS

Overview

This Guidance demonstrates how to build a real-time machine learning (ML) inferencing solution on AWS that can serve millions of requests per second. By hosting your solution’s ML model on Amazon Elastic Container Service (Amazon ECS) and routing requests to the ML server using Network Load Balancer, you can achieve low latency and support high-throughput inference requirements commonly found in real-time and programmatic advertising. This Guidance provides an example of applying ML for ad request filtering and demonstrates how to build a client application that can simulate high-throughput OpenRTB-based requests to send to the ML inference server.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon CloudWatch monitors the performance of the Amazon ECS cluster (including CPU and memory) along with the incoming requests sent through Network Load Balancer . Your CloudWatch dashboard—created as part of an AWS CloudFormation script—provides a comprehensive view of the number of incoming requests and their associated latency. By using CloudWatch to visualize and analyze performance and latency, you can better identify any bottlenecks in your application.

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By scoping down all AWS Identity and Access Management (IAM) policies to the minimum permissions required for the services to function properly, you can limit unauthorized access to resources.

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The Amazon ECS cluster runs a service definition that maintains a desired capacity of EC2 instances. If one of the instances becomes unavailable, a new instance will automatically launch and be registered with the Amazon ECS cluster as a healthy target to receive incoming requests routed by Network Load Balancer .

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Network Load Balancer , which communicates with Amazon ECS , supports low-millisecond latency and high throughput that are apt for this use case.

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Amazon EC2 Auto Scaling groups let you run your application at the desired capacity while providing dynamic support for scaling based on the load. Automatic scaling grows or reduces the infrastructure based on load and your scaling policy. This helps you control the costs associated with running your application.

Read the Cost Optimization whitepaper 

The Amazon EC2 -based Amazon ECS cluster lets you choose appropriate hardware types and configurations for specific workloads so that they run efficiently. As a result, you can maximize utilization and avoid overprovisioning resources. This Guidance is designed for low-latency and high-performance model inference workloads, so appropriate EC2 instance types are powered by AWS Graviton3 . This service uses up to 60 percent less energy for the same performance as comparable EC2 instances, helping you reduce your carbon footprint.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.