Workday Accelerates Generative AI / ML Product Development Using Amazon SageMaker

Overview

Workday uses Amazon SageMaker to let engineering teams build, train and deploy ML models, including LLMs, across AWS Regions to meet global customers' data-residency requirements. Using Amazon SageMaker Studio, Jumpstart and Ground Truth Plus, Workday piloted a closed-book ML application analyzing job descriptions, invoices and contracts, improving ML inference latency by a factor of five. Workday also received early access to Amazon Bedrock for generative AI prototyping. Head of Workday AI Shane Luke said the company has scaled from a thousand inference requests to tens of millions coming in daily.

The challenge

Workday needed to run ML inference in alignment with its global customers' data residency requirements, requiring a federated, distributed system that could run in many regions without investing in its own regional private clouds.

The solution

Workday adopted Amazon SageMaker to let engineering teams build, train and deploy ML models, including LLMs, across AWS Regions. Engineers use Amazon SageMaker Studio for collaborative development, SageMaker Jumpstart to compare and evaluate foundation models, and SageMaker Ground Truth Plus for human-feedback labeling across eight use cases including named entity recognition and sentiment analysis. Workday piloted a closed-book ML application analyzing job descriptions, invoices and contracts, and received early access to Amazon Bedrock for generative AI prototyping and testing multibillion-parameter models.

Reported business value

Workday's ML inference latency improved by a factor of five during the closed-book ML pilot, and the company scaled from a thousand inference requests to tens of millions coming in daily, with virtually no downtime.