BUILD is the key event for the Snowflake developer community. During BUILD, the company unveils the latest technical advancements that will help customers optimize their use of the Snowflake data platform.
This year, Snowflake announced a range of updates focusing on data and architecture, collaboration tools, and AI and ML. In this analysis, I’ll concentrate on the AI-related announcements regarding Cortex AI, Snowflake’s dedicated AI layer.
Agentic AI Advances
Cortex AI is a crucial component of the Snowflake Data Cloud, now referred to as the AI Data Cloud. This platform includes interoperable storage, elastic compute, and cloud services layers. Snowflake has announced a series of enhancements to Cortex AI, further preparing the technology to assist developers in the era of agentic AI.
Collectively, the new capabilities enable developers to easily create conversational applications within Snowflake while utilizing various Large Language Models (LLMs) to optimize specific tasks. In related news, Snowflake has also announced support for Container Runtime in Snowflake ML, which is now generally available through Snowflake Notebooks.
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“For enterprises, AI hallucinations are simply unacceptable. Today’s organizations require accurate, trustworthy AI in order to drive effective decision-making, and this starts with access to high-quality data from diverse sources to power AI models,” said Baris Gultekin, Head of AI, Snowflake.
“The latest innovations to Snowflake Cortex AI and Snowflake ML enable data teams and developers to accelerate the path to delivering trusted AI with their enterprise data so they can build chatbots faster, improve the cost and performance of their AI initiatives, and accelerate ML development.”
Faster GenAI App Building
Snowflake has announced a series of advancements in the capabilities of end-to-end conversational app development. Most significantly, the introduction of the Cortex Chat API will enable 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 enhance response quality, while new knowledge base connectors, such as the Snowflake Connector for SharePoint, will expedite the data ingestion process.
Furthermore, Snowflake’s new Cortex Knowledge Extensions on Snowflake Marketplace will facilitate third-party data integration for GenAI use cases. The company claims this is the only solution of its kind to fully adhere to intellectual property rights.
In terms of monitoring, AI Observability for LLM applications will allow users to measure relevance and latency for GenAI apps in development, while also enhancing compliance and governance processes.
Snowflake is also unveiling a range 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 better choices and increased flexibility for developers when deciding which LLM to apply in specific use cases, helping to ensure maximum efficiency at a lower cost.
Closing Thoughts
Snowflake is dedicated to enhancing its AI Data Cloud capabilities to eliminate data silos, improve security, and accelerate application and GenAI development. The availability of AI and ML tools for discovering and utilizing enterprise data is essential to these initiatives.
Now, with just a few API calls, it’s possible to create intelligent AI agents that can perform automated API calls. Snowflake is actively streamlining its data infrastructure architecture, which facilitates AI development.
All of this is conducted with built-in governance, access controls, observability, and safety measures to ensure that AI security and trust are top priorities. Snowflake is rapidly becoming a leading enabler of AI while maintaining its focus on data, which remains the company’s key differentiator.
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