Workday Accelerates Generative AI & ML Product Development Using Amazon SageMaker

Workday Inc. (Workday), a leading provider of solutions that help organizations manage their people and money, is highly focused on putting its engineering effort toward developing products that have built-in artificial intelligence (AI) capabilities. To help free its engineers from infrastructure maintenance, Workday adopted Amazon SageMaker, a fully managed service that helps its teams build, train, and deploy machine learning (ML) models for any use case. By using AWS services, Workday’s engineering teams can rapidly iterate and deploy complex models, including large language models (LLMs), to production.

For its generative AI use cases, Workday uses Amazon SageMaker to simplify searching, evaluating, customizing, and deploying LLMs. “Workday has been an early adopter of LLMs, and we are actively building new generative AI capabilities that will help our customers increase productivity, grow, retain talent, streamline business processes, and drive better decision-making,” says Eddie Raffaele, vice president of Workday AI. “Workday can quickly tap into the power of generative AI and realize its value by bringing the best solutions to customers safely and responsibly.”

To support collaboration across its global teams, Workday provides its engineers access to Amazon SageMaker Studio, a web-based, integrated development environment for ML. Workday’s engineers can then compare and evaluate new foundation models by using Amazon SageMaker Jumpstart, an ML hub with foundation models, built-in algorithms, and prebuilt ML solutions. “For tasks such as creating job descriptions, which must be high quality, we use the model evaluation capability in Amazon SageMaker and select the best foundation model that reflects our company’s priorities and metrics in a responsible way,” says Luke.

Workday’s engineering team has also adopted Amazon SageMaker Ground Truth Plus, which applies human feedback across the ML lifecycle to create and evaluate high-quality models. The team has used this solution across eight labeling use cases, including named entity recognition, entity linking, sentiment and theme analysis, and more. “There’s a lot of labeling and annotating that is needed to manage our LLM outputs and receive high-quality data within our guaranteed SLAs,” says Luke. “Amazon SageMaker Ground Truth Plus has become an intrinsic part of our LLMs.”

Next, its engineers can fine-tune their LLMs with high-quality data by using Amazon SageMaker Notebook Instances to prepare and process the data to train their LLM models. Workday’s engineers then deploy their models for inference to achieve optimal performance and costs while reducing operational burden. For example, Workday used Amazon SageMaker to pilot a closed-book ML application that could analyze job descriptions, invoices, and contracts. During this pilot, Workday saw its ML inference latency improve by a factor of five.

Workday also uses LLMs to power friendly, personalized reminders that help its customers stay on track with their project and organizational goals. “There are more than 13,000 tasks available through Workday,” says Luke. “We’ve built and trained an ML model for a tenant that delivers the three top task recommendations based on the user’s activity.” With these tools at their fingertips, Workday’s customers can maximize their operational efficiency and prioritize projects with data-driven insights.

Workday received early access to Amazon Bedrock, a service that provides the simplest way to build and scale generative AI applications with foundation models. Workday uses Amazon Bedrock to facilitate product prototyping and test multibillion-parameter ML models. “We’re able to rapidly experiment and identify which AI capabilities we should invest in and put in front of our customers,” says Luke.

The Workday team is also working toward immediate deployment of new features for its customers instead of rolling out features one region at a time. “We’re pleased with the flexibility that AWS has given us,” says Luke. “We can deliver value to our customers and scale horizontally.”