Remove Accessible Remove Document Remove Unstructured Data
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Top 5 Data + AI Predictions for Financial Services in 2024

Snowflake

The foundation for success is a data platform that allows flexible, cost-effective ways to access gen AI — whether organizations want to use off-the-shelf commercial and open-source large language models (LLMs), or fine-tune their own LLMs for more complex applications. Rinesh Patel, Snowflake’s Global Head of Financial Services 2.

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Fueling Enterprise Generative AI with Data: The Cornerstone of Differentiation

Cloudera

By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives. Structured data is highly organized and formatted in a way that makes it easily searchable in databases and data warehouses.

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Snowflake Cortex Search: State-of-the-Art Hybrid Search for RAG Applications

Snowflake

Snowflake Cortex Search, a fully managed search service for documents and other unstructured data, is now in public preview. Solving the challenges of building high-quality RAG applications From the beginning, Snowflake’s mission has been to empower customers to extract more value from their data.

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Use AI in Seconds with Snowflake Cortex

Snowflake

For organizations to fully capitalize on this potential, it’s critical that everyone — not just those with AI expertise — is able to access and use generative AI. With just a single line of SQL or Python, analysts can instantly access specialized ML and LLM models tuned for specific tasks. See Document AI in action on YouTube.

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5 Steps to Data Diversity: More Diverse Data Makes for Smarter AI

Snowflake

Diverse data provides a broader view and helps avoid the potential blinders traditional sources can perpetuate. To ensure your AI models are trained with as much data as possible, here are five best practices for greater data diversity: 1. Break down internal silos to access cross-functional sources.

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Introducing Vector Search on Rockset: How to run semantic search with OpenAI and Rockset

Rockset

In the demo, you’ll see how Rockset delivers search results in 15 milliseconds over thousands of documents. Organizations have continued to accumulate large quantities of unstructured data, ranging from text documents to multimedia content to machine and sensor data. Why use vector search?

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Accelerate AI Development with Snowflake

Snowflake

Conversational apps: Creating reliable, engaging responses for user questions is now simpler, opening the door to powerful use cases such as self-service analytics and document search via chatbots. For instance, if your documents are in multiple languages, an LLM with strong multilingual capabilities is key.