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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. The following screenshot shows an example of an interaction with Field Advisor.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

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For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

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Architect a mature generative AI foundation on AWS

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Generative AI applications seem simpleinvoke a foundation model (FM) with the right context to generate a response. Many organizations have siloed generative AI initiatives, with development managed independently by various departments and lines of businesses (LOBs). This approach facilitates centralized governance and operations.

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What Is a Lakebase?

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Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

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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.

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning Blog

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. The robust security features provided by Amazon S3, including encryption and durability, were used to provide data protection.

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

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Traditional data preprocessing methods, though functional, might have limitations in accuracy and consistency. This might affect metadata extraction completeness, workflow velocity, and the extent of data utilization for AI-driven insights (such as fraud detection or risk analysis).