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Be Warned: You Can’t Rely On Big Data!

Beyond Philosophy

And linking data points throughout a journey is a step in the right direction. But I have a big problem with Big Data. Because while Big Data can increasingly show you what your customers do, it cannot show you why they do it. Big Data can’t see the distinction because it doesn’t measure emotions.

Big data 284
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. To view this series from the beginning, start with Part 1.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. In the following sections, we explain how to deploy this architecture.

APIs 124
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

Data scientists across business units working on model development using Amazon SageMaker are granted access to relevant data, which can lead to the requirement of managing prefix -level access controls. Amazon S3 Access Points simplify managing and securing data access at scale for applications using shared datasets on Amazon S3.

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

AWS Machine Learning

He has extensive experience developing enterprise-scale data architectures and governance strategies using both proprietary and native AWS platforms, as well as third-party tools. Previously, Karam developed big-data analytics applications and SOX compliance solutions for Amazons Fintech and Merchant Technologies divisions.