Brave Software improves its web browser using machine learning on AWS

Improving machine learning predictions

The Brave team of two data scientists and a DevOps specialist created the browser functionality with a fairly basic deployment model. But to improve the local machine learning models advertisers depended on, the model pipeline had to be much more robust and scalable.

The first step for Brave Software was to move its machine-learning model creation to the cloud. Brave worked with Amazon Web Services (AWS) to make the move. When it came time to bring more power to its machine learning models, the AWS representative suggested working with a specialist in developing machine learning architectural patterns. The rep recommended  Rackspace Technology. Rackspace is a leading AWS Partner Network (APN) Premier Consulting Partner.

“I recommended Rackspace as a partner, based on their established expertise on AWS, and especially their past successful MLOps Foundations deployments for machine learning operations,” said Brennan Demro, an account manager at AWS. “What’s more, Rackspace takes a unique approach to working backward from the customer’s goals and building an engagement model that fits the customer to ensure success. This approach is exactly what Brave Software needed.”

Rackspace is one of only a few service providers to have achieved AWS Machine Learning Competency status. This designation recognizes Rackspace for demonstrating deep expertise in machine learning experience on AWS, and for delivering its solutions seamlessly in the cloud, solving organizations’ data challenges, and enabling machine learning and data science workflows. The unique combination of expertise in AWS services and machine learning made Rackspace an ideal partner for Brave.

Completing the project ahead of schedule

While the infrastructure component of thedevelopment project was straightforward, Brave had an aggressive deadline of six weeks. Plus, Brave modified the deployment model midstream and Rackspace had to shift gears.

Another challenge was modifying the existing machine learning model, which was a natural language processing model. It needed to be optimized to run on the cloud in a reasonable amount of time. This adjustment helped keep costs manageable.

The Rackspace team also trained the Brave team on how to use the unique MLOps framework, including how to deploy and troubleshoot it. Even with the needed changes to the model’s architecture, Rackspace completed the deployment ahead of schedule.

Making the difference in growth potential

Along with critical improvements in its machine learning models, Brave also gained several benefits from its Rackspace partnership. The model design through MLOps Foundations helped Brave accelerate training, publishing and deploying new models to provide better predictions for browser advertisements.

Model training was reduced from several days to six hours. Also, the streamlined deployment process requires only a few hours instead of a few days, as it previously had. And infrastructure costs dropped by 50%, thanks to purchasing spot instances on an AWS auction service instead of purchasing a dedicated platform.

Brave’s software quality has improved thanks to a new notification and approval process that it didn’t have before. Now, when machine learning model training is complete, the team can review the metrics and accept or reject the updates before they are deployed to production.

“Having this essential QA functionality helps us deliver more robust browser software to our users,” said Secretan.