Snowflake Unveils LLM-powered Document AI and Other Innovations at Annual Conference

Snowflake Inc., a leading cloud-native data warehousing and analytics platform, recently revealed multiple upcoming innovations and new products at its annual user conference, Snowflake Summit.

At the event, held virtually on March 2nd due to the ongoing pandemic, the company showcased over 30 new product updates, including Snowflake document AI, a cloud-native solution powered by its recently-acquired machine learning products, Lessenger-Lane-Miller (LLM). The LLM technology enables customers to connect their data sources such as spreadsheets and websites to Snowflake and then use AI to automatically recognize, classify, and extract relevant information from documents in order to better analyze and manage them.

In addition to document AI, Snowflake unveiled other new features and enhancements, including the launch of Snowflake Mobile, a new app that allows users to gain secure, real-time insights into their data; Snowflake Spot, an automated cloud instance provisioning service that enables customers to quickly and easily access cost-effective, purpose-built cloud compute clusters; and Snowflake Elastic Data Warehouse, a fully-managed, massively parallel computing system that makes it easier to scale business operations quickly and efficiently.

Snowflake also listed other key advancements, such as its new Machine Learning Model Automation Architecture, which enables Snowflake Data Platform customers to quickly and easily connect and run their favorite ML models in Snowflake; Snowflake Data Predict, a service for making more accurate predictions faster; and the release of the Snowflake Cloud Data Exchange, which simplifies the process of connecting to data from other cloud platforms.

Overall, the product updates and announcements from Snowflake demonstrate its commitment to helping ease the burden of managing and analyzing data in the cloud. With the new innovations revealed at the user conference, Snowflake customers and partners will be well-positioned to take full advantage of the scalability, power, and performance of their data operations.