Snowflake Cortex AI enhancements improve trustworthy AI functionality

BUILD is the premier event for the Snowflake developer community. During BUILD, the company will showcase the latest technical advances that will help customers optimize their use of the Snowflake data platform.

This year, Snowflake announced a series of updates focused on data and architecture, collaboration tools, and AI and ML. In this analysis, I will focus on the AI-related announcements about Cortex AI, Snowflake's dedicated AI layer.

Advances in Agent AI

Cortex AI is a critical component of the Snowflake Data Cloud, now referred to as the AI ​​Data Cloud. This platform includes interoperable storage, elastic compute and cloud service layers. Snowflake has announced a number of improvements to Cortex AI, further preparing the technology to support developers in the age of agent AI.

Overall, the new features enable developers to easily build conversational applications in Snowflake and leverage various Large Language Models (LLMs) to optimize specific tasks. In related news, Snowflake also announced support for Container Runtime in Snowflake ML, now generally available through Snowflake Notebooks.

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“For companies, AI hallucinations are simply unacceptable. Today’s businesses need accurate, trustworthy AI to drive effective decision-making, and that starts with access to high-quality data from diverse sources to power AI models,” said Baris Gultekin, Head of AI at Snowflake.

“The latest innovations in Snowflake Cortex AI and Snowflake ML enable data teams and developers to accelerate the path to delivering trustworthy AI with their enterprise data so they can build chatbots faster, improve the cost and performance of their AI initiatives, and accelerate ML development can.” “

Faster GenAI app creation

Snowflake has announced a number of advancements in the development of end-to-end conversation apps. Most importantly, the introduction of the Cortex Chat API will allow developers to combine structured and unstructured data into a unified REST API call, reducing orchestration and integration tasks.

Additionally, multimodal support for LLMs, including Meta's Llama 3.2 models, will improve response quality, while new knowledge base connectors, such as the Snowflake Connector for SharePoint, will accelerate the data ingestion process.

Additionally, Snowflake's new Cortex Knowledge Extensions on the Snowflake Marketplace will facilitate third-party data integration for GenAI use cases. The company claims this is the only solution of its kind that fully respects intellectual property rights.

In terms of monitoring, AI Observability for LLM Applications will enable users to measure relevance and latency for GenAI apps in development while improving compliance and governance processes.

Snowflake also introduces a number of new customization options for large batch text processing, along with a more comprehensive selection of pre-trained LLMs for Cortex AI. Ultimately, these innovations provide developers with greater choice and flexibility in deciding which LLM to apply in specific use cases, helping to ensure maximum efficiency at lower costs.

Final Thoughts

Snowflake is dedicated to improving its AI Data Cloud capabilities to eliminate data silos, improve security, and accelerate application and GenAI development. The availability of AI and ML tools to discover and use enterprise data is critical to these initiatives.

Now, with just a few API calls, it is possible to create intelligent AI agents that can make automated API calls. Snowflake is actively optimizing its data infrastructure architecture, which makes AI development easier.

All of this is done with built-in governance, access controls, observability and security measures to ensure AI security and trust are top priorities. Snowflake is quickly becoming a leading enabler of AI while maintaining its focus on data, which remains the company's key differentiator.

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