Jobcase Scales ML Workflows to Support Billions of Daily Predictions Using Amazon Redshift ML
Jobcase, an online community for workers, is using Amazon Redshift ML to make billions of machine learning predictions per day, improving member engagement by 5 percent without increasing costs.
Benefits
members supported through improved scalability
minutes to make billions of predictions, instead of 4 to 5 hours
Overview
As an online community for workers and job seekers, Jobcase uses machine learning (ML) models to analyze its database of millions of job listings to match members with job recommendations. With more than 20 million unique visitors per month, the site scores billions of matches every day. To support this workload, the company needed to improve the scalability of its ML-based job-search recommendation engine while remaining cost efficient.
Jobcase was already using Amazon Web Services (AWS) to ingest and store over 100 TB of compressed data. But the company wanted to significantly reduce the need to move large amounts of that data between Amazon Redshift—a data warehouse that makes it simple to query and combine exabytes of structured and semistructured data—and its ML environment. Using Amazon Redshift ML, which analysts can use to create, train, and apply ML models using familiar SQL commands in Amazon Redshift, Jobcase can perform predictions on billions of records in a matter of minutes. Using AWS, Jobcase has improved scalability while decreasing its cost-to-performance ratio. Now, the company can support its growing community efficiently and test new features more quickly.

About Jobcase
Jobcase is an online community dedicated to empowering and advocating for the world’s workers. Its technology offers access to jobs, tools, resources, and community-powered knowledge, helping more than 110 million members prepare for any role.

Amazon Redshift is one of the most important tools we have in growing Jobcase as a company.
Ajay Joshi
Distinguished Engineer, JobcaseAWS Services Used
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