Remove Accessible Remove Demo Remove Unstructured Data
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Calling All Builders: Get Hands-On With AI and Apps

Snowflake

Register now and join thousands of fellow developers, data scientists and engineers to learn about the future of AI agents, how to effectively scale pandas, how to create retrieval-augmented generation (RAG) chatbots and much, much more. From Snowflake Native Apps to machine learning, there’s sure to be something fresh for everyone.

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

Rockset

To highlight these new capabilities, we built a search demo using OpenAI to create embeddings for Amazon product descriptions and Rockset to generate relevant search results. In the demo, you’ll see how Rockset delivers search results in 15 milliseconds over thousands of documents. Why use vector search?

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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 demo here.

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Snowflake Startup Challenge 2024: Announcing the 10 Semi-Finalists

Snowflake

The Innova-Q dashboard provides access to product safety and quality performance data, historical risk data, and analysis results for proactive risk management. Titan Systems Titan helps enterprises to manage, monitor and scale secure access to data in Snowflake with an infrastructure-as-code approach.

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Educating ChatGPT on Data Lakehouse

Cloudera

The one key component that is missing is a common, shared table format, that can be used by all analytic services accessing the lakehouse data. The table format provides the necessary structure for the unstructured data that is missing in a data lake, using a schema or metadata definition, to bring it closer to a data warehouse.

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The Moat for Enterprise AI is RAG + Fine Tuning – Here’s Why

Monte Carlo

And in many ways, LLMs are going to make data engineers more valuable – and that’s exciting! Still, it’s one thing to show your boss a cool demo of a data discovery tool or text-to-SQL generator – it’s another thing to use it with your company’s proprietary data, or even more concerning, customer data.