We believe that the most impactful, open problems in applied machine learning have yet to be solved.

We want ML engineers to eliminate bugs and optimize their model inference performance easily.

It all started when our founder, Zheng, started dabbling with machine learning early in his career and eventually landed at Cruise AI's Perception Platform & Machine Learning Accelerators (MLA) team in 2019. It was there where he and his peers learned how challenging it was to even deploy models that our researchers have developed.

Everything from managing datasets, to validating our models to not just be functionally correct, but fit within the system performance limits of our hardware constrained autonomous vehicles. The model deployment cycles were always longer than we'd like them to be.

Many of these deployment tools / solutions weren't readily available, so we had to build them from scratch. Imagine a world where  Stripe doesn't exist and having to build billing for a modern software stack. That's how painful it was and still is.

Zheng has heard from several ML engineers at other autonomous vehicle companies who also face similar pain points. Some came from different industries with a business need for more optimized models but can't justify the R&D costs for better deployment tooling. Wouldn't it be nice if everybody could benefit from a solution focused solely on this vertical? What if Cellulose could fill this gap?

Want to optimize your machine learning models and also shave weeks of debugging pain?
Spend more time building new model features than worrying if they'd run within your production system performance parameters.
Get Started