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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

The Amazon Bedrock VPC endpoint powered by AWS PrivateLink allows you to establish a private connection between the VPC in your account and the Amazon Bedrock service account. Use the following template to create the infrastructure stack Bedrock-GenAI-Stack in your AWS account. You’re redirected to the IAM console.

APIs 127
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Contact Center Trends 2021: The CX Watershed

Fonolo

I see the CX industry finding new, faster and more efficient ways to meet their customers’ needs, maybe through the use of new technology, while also balancing a deeply personal, empathetic human connection.”. The investments in infrastructure and technology to make remote working possible won’t vanish.

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Bank Branch Innovation Like Never Before: 5 Brands Redefining Tradition

Avaya

Consider Bank of America’s new ATM Teller Assist , a next-gen offering that “combines the technology and convenience of an ATM with the human touch of a teller.” The technology has been activated in four test cities including Miami and San Francisco, with 6,000 more already upgraded and ready to go.

Banking 58
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Halo Smart Labs Develops a Smarter Smoke Alarm: IoT at It’s Best

Natalie Petouhof

The issue with old smoke detector technology was their limited functionality: • Inability of traditional smoke detectors to discern between a real disaster such as a home fire and a minor issue such as burnt toast. It was created in 2012 after a brush with tragedy. Why was Halo Smart Labs created? The future of IoT is very bright.

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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

How to use MLflow as a centralized repository in a multi-account setup. Prerequisites Before deploying the solution, make sure you have access to an AWS account with admin permissions. Serverless technologies are the perfect candidate to satisfy all these requirements with minimal operational overhead.

APIs 71
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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

To complete this tutorial, you must have the following prerequisites: Have an AWS account. If you don’t have an account, you can create one. Set up Lake Formation permissions using a data cell filter for automatically detected columns, and restrict the columns to the data scientist persona: lakeformation = boto3.client('lakeformation')