AI is taking over the world of data. More and more people are using Large Language Models (LLM) such as ChatGPT and Google Bard every day and seeing the potential they possess for almost every organization. Snowflake AI Data Cloud has created a set of AI models focused on enterprise level tasks that it calls the Arctic.Â
In this blog, we’ll explore the Arctic, its key features, and some top use cases for organizations to use it.
What is Snowflake Cortex?
Snowflake Cortex is a service that allows you to use both machine learning and artificial intelligence (AI) functionalities directly within your Snowflake environment. You can use AI and ML functions on your data without leaving Snowflake’s secure and scalable platform.Â
With Cortex, you can use LLM functions such as summarizing text data, translating language data, and extracting information from structured and semi-structured data.
What is Snowflake Arctic?
As an extension of Cortex, Snowflake has created Arctic, a family of enterprise-grade models. The family includes models built for information retrieval and an LLM that excels at business-oriented tasks such as information retrieval, generating SQL, and writing various types of code. It does all this in a more performant and cost-effective way than other LLM models in the market.Â
Arctic Features
Performance
Most open-source LLMs are built for a broad range of tasks. The Arctic, however, focuses specifically on enterprise-oriented needs. This focused approach optimizes the model for tasks like code generation and SQL query execution, leading to superior performance in these areas.
Efficiency
Arctic utilizes a technique called Dense Mixture-of-Experts (MoE), which allows it to achieve high performance with surprisingly few active parameters compared to other models. This translates to significant cost savings for businesses deploying Arctic LLM at scale, as training and running large LLMs can be computationally expensive.
Open Source
Unlike many high-performing LLMs, Arctic is released under an Apache 2.0 license. This means its code is freely available for anyone to use for any purpose, including commercially. This openness helps foster collaboration within the AI community and allows businesses to customize the model for their specific needs.
Seamless Snowflake Integration
One of Arctic’s best features is that it’s designed to be used within Snowflake, where your data already resides. Because it’s in your account, you get all the security and scalability of Snowflake while using the LLM on your data.Â
Arctic Top Use Cases
SQL co-pilot
This is exactly what the Arctic was designed for: creating queries based on your data quickly and efficiently using natural language. Having complex queries written for them will give data analysts more time to dig deeper into insights without being bogged down by SQL syntax.
Code co-pilot
Arctic isn’t only for SQL, though. It can also be used as a code companion for developers. It can suggest code completion as developers work, review, and revise existing code, and it can automate repetitive tasks. Automating more of the coding removes many human errors and streamlines the development process.Â
Data Exploration
The Arctic also has significant capabilities in Natural Language Processing, which can be used to comb through and analyze data through natural language queries. This allows users to ask questions about their data more intuitively, fostering data exploration and uncovering hidden insights that might be missed in traditional data analysis methods.
Closing
Arctic and other models like it offer unprecedented capabilities for enterprises, transforming data workflows, boosting productivity, and unlocking valuable insights. AI is just starting to take off, especially in the data space, and SQL co-pilots and natural language processing are only the beginning of what we’ll be able to do with AI, such as Arctic.
If you’re wanting to make the most of new AI and ML technologies, phData offers expert help and custom solutions to fit your needs. With phData’s support, you can easily use advanced models in your work.
