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AWS re:Invent 2024 Highlights: Top takeaways from Swami Sivasubramanian to help customers manage generative AI at scale

AWS Machine Learning Blog

We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressionsand to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications. Canva uses AWS to power 1.2

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How AWS sales uses Amazon Q Business for customer engagement

AWS Machine Learning Blog

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

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The end-to-end workflow features a supervisor agent at the center, classification and conversion agents branching off, a humanintheloop step, and Amazon Simple Storage Service (Amazon S3) as the final unstructured data lake destination. Make sure that every incoming data eventually lands, along with its metadata, in the S3 data lake.

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What Is a Lakebase?

databricks

Separation of storage and compute : Lakebases store their data in modern data lakes (object stores) in open formats, which enables scaling compute and storage separately, leading to lower TCO and eliminating lock-in. At zero, the cost of the lakebase is just the cost of storing the data on cheap data lakes.

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Build a financial research assistant using Amazon Q Business and Amazon QuickSight for generative AI–powered insights

Flipboard

According to a Gartner survey in 2024 , 58% of finance functions have adopted generative AI, marking a significant rise in adoption. Their information is split between two types of data: unstructured data (such as PDFs, HTML pages, and documents) and structured data (such as databases, data lakes, and real-time reports).

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Lets assume that the question What date will AWS re:invent 2024 occur? The corresponding answer is also input as AWS re:Invent 2024 takes place on December 26, 2024. If the question was Whats the schedule for AWS events in December?, is within the verified semantic cache. Query processing: a.

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Integrate foundation models into your code with Amazon Bedrock

AWS Machine Learning Blog

Prerequisites Before you dive into the integration process, make sure you have the following prerequisites in place: AWS account – You’ll need an AWS account to access and use Amazon Bedrock. You can interact with Amazon Bedrock using AWS SDKs available in Python, Java, Node.js, and more.

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