Exploring Snowflake Cortex: A New Era for Business Through AI

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Introduction

Gen AI and Large Language Models (LLMs) are continuously transforming the technology landscape. Individuals and companies alike are always exploring the best ways to leverage these new tools to enhance productivity, drive innovation, create new products and services, and extract more value from their data assets.

The market currently experiences considerable hype in this space, with everyone from technology vendors to consultancies to C-level executives discussing the need to engage with these innovations. However, seasoned data leaders recognize that fully adopting and realizing value from Gen AI and LLMs requires several key components.

A critical piece of this puzzle is having a highly scalable, modern, cloud-based data architecture that provides a solid foundation for ingesting, processing, transforming, cleaning, and governing data at scale. Achieving this is no easy feat. Many companies are still navigating this journey, all at varying levels of maturity.

This situation leads to skepticism within the data community about the actual value of Gen AI and LLMs. Technical demos and talks at major technology conferences often highlight these cutting-edge developments. Yet, a minority of attendees are in a position to truly capitalize on these features based on my experience.

Even with a strong, resilient data platform, a persistent challenge, which is neither new nor specific to these tools, is embedding analytical processes into operational, productized workflows.

Technology vendors are addressing this 'path to production' challenge by developing services that integrate seamlessly with existing production data. If your core data foundations are already in place, bringing Gen AI and LLMs to your data—and crucially, making them easy to use—should facilitate easier adoption.

Snowflake now offers Snowflake Cortex, a suite of services designed to provide businesses with easy access to advanced large language models (LLMs). The Cortex service enables companies to integrate cutting-edge AI technologies with their existing data systems securely and efficiently.

The Relevance of Snowflake Cortex to Your Business

For businesses aiming to improve their operations through AI, Snowflake Cortex provides a significant benefit. By integrating AI directly with governed data, companies can achieve secure, efficient, and effective utilization of generative AI. This integration is essential for preserving data privacy and security while maximizing the advantages of AI.

Understanding Snowflake Cortex

Snowflake Cortex is a managed service that enables businesses to easily deploy large language models. These models, which are the driving force behind generative AI applications, can be accessed directly within the Snowflake environment. The service streamlines the integration of AI capabilities by managing complex tasks such as model optimization and GPU infrastructure management. This allows businesses to focus on leveraging AI effectively, rather than on its maintenance.

As illustrated in the diagram below, Snowflake Cortex essentially serves as a collection of AI and LLM functions that can be invoked within the Snowflake environment. Importantly, these functions operate on top of your clean, secure, and governed data that already resides in Snowflake.

Snowflake has developed a suite of services built on top of Snowflake Cortex to enhance data interaction and analysis:

• Document AI This service can read unstructured data from documents, such as PDF files, and builds a model that enables users to ask questions in natural language and receive answers. (Refer to the image below for more details.)
• Universal Search This feature allows users to search for database objects within their Snowflake account, as well as data products and Snowflake Native Apps available on the Snowflake Marketplace.
• Copilot An LLM-powered assistant that helps generate and refine SQL queries using natural language. Analysts simply ask Snowflake Copilot a question, and it formulates a SQL query using the relevant tables.

Challenges of Traditional AI Implementation Processes

Before the advent of integrated platforms like Snowflake Cortex, businesses faced numerous hurdles when implementing analytical models. Traditionally, leveraging machine learning (ML) involved data scientists extracting large amounts of data from a central data warehouse and moving it to external systems, such as SAS, for processing.

This method presented several challenges:

• Latency: Moving data between systems introduced significant delays, hindering real-time data analysis and decision-making—crucial for businesses needing timely insights.
• Data Redundancy: Duplication of data across systems was common, leading to increased storage costs and complexity in managing data consistency.
• Loss of Visibility and Control: Once data left the central warehouse, it became difficult to maintain oversight over who accessed the data and how it was used. This lack of control posed risks in terms of data security and governance.
• Operational Complexity: Supporting multiple systems not only increased the operational burden but also required teams to possess expertise in various technologies. This complexity could lead to inefficiencies and higher costs in terms of both time and resources.
• Integration Issues: Integrating results back into the main data system after processing in an external system often required additional effort and could lead to further complications, such as data mismatches or errors.

How Snowflake Cortex Addresses These Challenges

Snowflake Cortex addresses these challenges by keeping all operations within Snowflake's secure and managed environment. This integrated approach eliminates the need to export data to external systems, thereby reducing latency and data redundancy. It also allows data teams to maintain better visibility and control over their data, ensuring consistent adherence to security and governance standards.

By simplifying the technical landscape, Snowflake Cortex reduces operational complexity and the need for specialized knowledge across multiple systems. The platform's compatibility with various large language models and its built-in GPU infrastructure further streamline the process, enabling companies to focus more on strategic AI initiatives rather than on technical maintenance.

What Can Snowflake Cortex Do for You?

With Snowflake Cortex, businesses can engage in a variety of AI-driven activities without requiring extensive technical knowledge:

• Text Analytics and Generation: Companies can analyze text data and generate content, such as personalized emails, to improve customer engagement and conversion rates. For instance, video-hosting platforms can use this capability to effectively segment users and enhance subscription models.
• AI-Powered Chatbots: Organizations can create intelligent chatbots that pull information from extensive document databases, aiding in knowledge sharing and reducing the time spent searching for information. This is particularly beneficial for companies with large, dispersed teams.
• Capabilities and Support: The platform supports a range of models, including Snowflake’s own Arctic model, Google’s Mistral AI, and other third-party models like Llama 3 and Reka-Core. These models cater to various tasks from simple text generation to complex problem-solving across different data types including text, images, and video.

Why It Matters

The seamless integration of powerful tools like Snowflake Cortex into business operations democratizes access to AI, enabling more companies to leverage this technology to drive innovation and efficiency.

Snowflake ensures a high level of data privacy and security with its operations:

• Data Privacy: Snowflake does not use customer data to train any LLMs for cross-customer applications. LLMs operate within Snowflake, ensuring that data never leaves the Snowflake service boundary or is shared with any third-party provider.

• Security and Access Management: The existing security model established in Snowflake can be extended to manage access to Cortex LLM Functions, maintaining stringent control over who can use these advanced capabilities.

Conclusion

Snowflake Cortex represents a significant advancement for businesses aiming to implement AI. With its robust support for various models and seamless integration within the secure environment of Snowflake, Cortex is poised to be a key player in transforming business through AI. Ignore it at your peril!

To stay up to date with the latest business and tech trends in data and analytics, make sure to subscribe to my newsletter, follow me on LinkedIn, and YouTube, and, if you’re interested in taking a deeper dive into Snowflake check out my books ‘Mastering Snowflake Solutions’ and ‘SnowPro Core Certification Study Guide’.

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About Adam Morton

Adam Morton is an experienced data leader and author in the field of data and analytics with a passion for delivering tangible business value. Over the past two decades Adam has accumulated a wealth of valuable, real-world experiences designing and implementing enterprise-wide data strategies, advanced data and analytics solutions as well as building high-performing data teams across the UK, Europe, and Australia.?

Adam’s continued commitment to the data and analytics community has seen him formally recognised as an international leader in his field when he was awarded a Global Talent Visa by the Australian Government in 2019.

Today, Adam is dedicated to helping his clients to overcome challenges with data while extracting the most value from their data and analytics implementations. You can find out more information by visiting his website here.

He has also developed a signature training program that includes an intensive online curriculum, weekly live consulting Q&A calls with Adam, and an exclusive mastermind of supportive data and analytics professionals helping you to become an expert in Snowflake. If you’re interested in finding out more, check out the latest Mastering Snowflake details.