Develop repeatable, scalable processes that allow developers to gather, analyze, and store terabits of sensor data. By tracking what products are on the shelf, the Intelligent Retail Lab (IRL) seeks to improve customer experience by ensuring products are fresh, particularly in produce and meats.
Use Chef Enterprise Automation Stack (EAS) to build and maintain secure cloud and on premise instances and to automate the software build lifecycle for complex, fast-evolving analytic applications.
IRL is now able to use Chef Habitat to fully automate almost all applications and dependencies in its cutting-edge AI environment, enabling development at velocity.
- Developers are now focused on solving problems and not fighting the platform
- Chef Habitat with OpenCV eliminates the need for developers to constantly reconfigure the application, saving months of configuration time
- Chef EAS gives IRL the monitoring and control needed to leverage AI in their solutions.
About Walmart IRL
The Intelligent Retail Lab (IRL) is a technology incubator inside of Walmart dedicated to harnessing the power of computer vision, machine learning and artificial intelligence to help customers shop smarter. They believe that technology has a greater purpose: empowering people. By harnessing the power of artificial intelligence, they can create conveniences that benefit everyone. By experimenting with these new capabilities in IRL, they’ll explore how to make shopping better now and into the future.
Driving Innovation with the Chef Enterprise Automation Stack
IRL is using Computer Vision, AI, and machine learning technology to make customer’s shopping experiences better. Learn how they developed repeatable, scalable processes that allow developers to gather, analyze, and store terabits of sensor data with the Enterprise Automation Stack.
Increasing Developer Productivity with Chef Habitat
Using Chef Habitat developers at IRL were able to save months of time configuring and rebuilding OpenCV. Instead of each developer building and rebuilding their own environments they were able to leverage a shared build plan, make necessary changes, and then build and test locally.