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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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Ebi.Ai Launches Free Coronabot

CSM Magazine

has applied nearly 20 years of collective experience working with big data, analytics and systems integration to discover, market and deploy a range of natural, valuable tools for all businesses across multiple sectors including automotive, insurance, property, public sector and transport & travel.

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

However, sometimes due to security and privacy regulations within or across organizations, the data is decentralized across multiple accounts or in different Regions and it can’t be centralized into one account or across Regions. Each account or Region has its own training instances.

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5 Reasons to Boost Self-Service Using AI in Today’s Property Market

CSM Magazine

Programmed to answer more than 150 queries, residents are able to ask questions from: “How do I set up an account with my energy supplier?” to “Where’s the nearest pizza place?” and “What’s happening around here this week?” For more information, please visit www.ebi.ai.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Her skills and areas of expertise include data science, machine learning, and big data.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. About the Authors Charalampos Grouzakis is a Data Scientist within AWS Professional Services. Her skills and areas of expertise include data science, machine learning, and big data.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

User preference alignment – By taking into account a user profile that signifies user preferences, potential recommendations are better positioned to identify content characteristics and features that resonate with target users. He is deeply passionate about applying ML/DL and big data techniques to solve real-world problems.