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Introduction to Snowflake and AWS for Generative AI
About the Speaker
- The speaker is Matt Marillo, a Partner Engineer at Snowflake.
- He works with product teams at Snowflake and AWS to ensure their platforms work well together for AI and Generative AI workloads.
- He also works with individual customers to help them understand how to better use Snowflake and AWS for Generative AI.
What is Snowflake?
- Snowflake is a cloud-native data platform that has been around for 12 years.
- It started as a data warehouse solution and has since grown into a more comprehensive platform.
- Snowflake offers features such as separation of storage and compute, near-limitless scalability, low administration, and ease of use.
- Snowflake has native integrations with the entire AWS ecosystem, including services like S3, Glue, EMR, Lambda, Sage, and Bedrock.
AWS's Generative AI Stack
- AWS's Generative AI stack includes:
- Infrastructure: Trinium, Inferentia chips, and Nitro
- Bedrock: Foundational models and the ability to fine-tune models
- Amazon Q: A popular new service for Generative AI
Snowflake's Generative AI Capabilities
- Snowflake has Generative AI features and functionality under the "Cortex" brand:
- LLMs: Snowflake has its own homegrown models (Arctic family), as well as models from NVIDIA and LLaMA.
- Cortex Search: A Generative AI-powered search service for unstructured data in Snowflake.
- Cortex Analyst: A Generative AI-powered service for structured data in Snowflake, with high accuracy expectations.
- Document AI: Ability to parse and structure data from scanned documents using Generative AI.
Generative AI Use Cases in Production
- Snowflake is seeing two main use cases for Generative AI in production:
- Automated BI and conversational assistance: Allowing users to chat with data and get insights.
- Batch processing of text data: Summarizing and synthesizing unstructured data for better utilization.
Integrating Snowflake and AWS for Generative AI
- Orchestrating from Snowflake:
- Use Snowflake data as context and connect to external services like Bedrock using an external access connector.
- Wrap a function around the external service and expose it via a Streamlet app.
- Orchestrating from AWS:
- Use Bedrock agents with Cortex Analyst or Cortex Search as action items in the agent workflow.
- Set up a plugin in Amazon Q to connect to Snowflake Cortex services.
Demo: Integrating Cortex Search in Snowflake with Amazon Q
- Demonstrated setting up a Cortex Search service in Snowflake for Airbnb listing data.
- Showed how to integrate the Cortex Search service with Amazon Q using a plugin.
- Allowed prompting the Cortex Search service from Amazon Q and getting relevant search results.
Calls to Action
- Try out the quick starts at snowflake.com/quickstart to test Snowflake and AWS integrations.
- Check out the Bedrock agent workflow demo workshop published by AWS.
- Reach out to the Snowflake and AWS teams for direct support and collaboration.