Can’t forget about the massive opportunity in AI that Mongo is especially well built for. Since we don’t talk about NVDA too much these days, there’s still a massive market out there.
Many of us had a lot of success with NVDA, riding the S-curve train (maybe a stop or two too long at least for me). All that infrastructure is being put to work. The world needs Mongo for the next phase. The software that powers this next phase will be the next big AI boom IMO.
Perhaps started working with Open-source version, but not for long.
This type of approach was limiting the velocity with which Continental could innovate and so they moved from SQL-based tools to MongoDB to build their deep learning framework. Originally Continental only planned to use MongoDB to store and label image data, such as scenes from the road. But the team quickly found that they can use the same database for the analytical image data, the derived metadata and the results of their experiments, significantly increasing productivity.
With MongoDB’s flexibility and parallelism, developers can build new models more rapidly, work together without sacrificing speed and accuracy, and quickly build and test new prototypes for the autonomous SensePlanAct framework. “In the end we were able to tame this deep learning beast with this flexible database”, says Martin Berchtold-Buschle, who is the subject matter expert for Big Data Infrastructure & Deep Learning at Continental.
Looking into the future, Continental is moving toward the cloud and plans to adopt MongoDB Atlas, the fully automated database as-a-service. The team believes that MongoDB will be a crucial component in helping them achieve Vision Zero which is being adopted as a new standard of safety across many cities and governments around the world.
https://www.mongodb.com/blog/post/mongodb-helps-bring-new-er…
Darth