How is Snowflake enabling LLMs and ML applications?

Machine learning models require massive amounts of data for training and deployment. When relevant data is spread across numerous source systems, looking for and requesting access to data significantly slows development. Snowflake provides a single point of access to a global network of trusted data. With Snowflake, you can bring nearly all data types into your model without complex pipelines and enjoy native support for structured, semi-structured (JSON, Avro, ORC, Parquet, or XML), and unstructured data.

Build LLM-powered data apps

Data scientists no longer need to be tethered to a front-end developer to build intuitive, easy-to-use data apps. Using Streamlit, a pure-Python open-source application framework, data scientists can quickly and easily create beautiful, intuitive data applications. With Streamlit, Snowflake users can use LLMs to build apps with integrations to web-hosted LLM APIs using external functions and Streamlit as an interactive front end for LLM-powered apps.

Aggregate and analyze unstructured data

Unstructured data is one of the fastest-growing data types, but historically, there was no easy way to aggregate and analyze that data. To continue securely offering, discovering, and consuming all types of governed data, a purpose-built, multi-modal LLM for document intelligence.

Interactive data search

Snowflake’s recent acquisition of Nerva is accelerating data search through generative AI. It enables conversational paradigms for asking questions and retrieving information, allowing teams to discover precisely the right data point, data asset, or data insight.

The Snowflake Data Cloud’s scalability, flexibility, and performance provide a powerful foundation for LLM-enabled machine learning applications. Snowflake paves the way for unlocking the capabilities of LLMs, including enhanced language understanding, text generation, and advanced analytics at scale.