liveBook · Manning

AWS Application Load Balancer

As explained in Machine Learning System Design

AWS Application Load Balancer is a managed service that distributes application traffic across multiple targets using application-level routing.

AWS Application Load Balancer distributes real-time inference requests across a pool of EC2 servers when high load and low latency make serverless scaling difficult. section 15.5.2

High-load inference serving

PhotoStock places stateless inference workers behind the load balancer, keeps a reasonable number of workers available, and enables additional scaling. Spot instances can reduce cost because terminated workers are replaceable. section 15.5.2

How Machine Learning System Design covers it

How deep it goes
Connects load balancing to stateless workers, guaranteed capacity, scaling, and spot replacement, but defers hardware sizing to experiments.
Examples
Uses EC2 inference servers behind an AWS Application Load Balancer for PhotoStock’s high-load, low-latency queries.
What it assumes
Assumes real-time inference and EC2-based servers, while leaving exact instance and GPU sizing for later experiments.
What you can do after
Operate/configure real-time inference with EC2 workers behind an AWS Application Load Balancer while planning capacity and replacement.
How it connects
Connects real-time inference, Amazon EC2, spot instances, serverless computing, scaling, and stateless workers.

Also explained in

Compare how 2 books explain AWS Application Load Balancer →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.