[Ed's TALK] Graviti Seeks to Corral Unstructured Data for AI

Our CEO Edward Cui recently joined an interview with Alex Woodie from Datanami.

Graviti Seeks to Corral Unstructured Data for AI

In many ways, unstructured data is the bane of modern data collectors. Compared to the svelte nature of structured data, such as numbers safely tucked away in databases, unstructured data like text and images is huge, messy and difficult to handle.

Our CEO Edward Cui recently joined an interview with Alex Woodie from Datanami and shared his past working experience, how Graviti grew from an idea to a fast-growing startup and how Graviti Data Platform efficiently drive innovation in the management and application of unstructured data resources for unstructured data.

How to manage and use the structured data is the core proposition of the next era, thus Graviti, which came out of stealth a week ago, aims to address some of the big challenges that data scientists and AI engineers face in using unstructured data to train machine learning algorithms.

“As the self-driving industry exploded, the problem of redundant unstructured data was more significant for AI developers, and it was a key barrier in the entire AI industry." Cui tells Datanami, "The challenge prompted me to build the Graviti Data Platform, which is a modern data infrastructure designed for unstructured data at scale.”

Gravit’s core goal with the Graviti Data Platform is to reduce the amount of time users spend doing the drudge work of managing data, freeing them to spend more time developing models, which is what AI developers ultimately want to do.

Data version control, data visualization, and team collaboration are our key product features that help engineering teams to increase their productivity in data management and model training,” Cui explains. “The platform adopted a Git-like structure for managing data versions and collaborating across teams. Role-based access control and visualization of version differences allow your team to work together safely and flexibly. The end result is that Graviti liberates developers from chores, and they can now spend more time analyzing unstructured data and training models.”

If you want to get more views of Cui, you could click here to read full article from Datanami.