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

AWS Machine Learning

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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Redacting PII data at The Very Group with Amazon Comprehend

AWS Machine Learning

This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group , which operates digital retailer Very, security is a top priority in handling data for millions of customers. Overview of solution. The adoption of Logstash was initially done seamlessly.

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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service provided by AWS that offers developers access to foundation models (FMs) and the tools to customize them for specific applications. It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure.

APIs 126
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. Wipro is an AWS Premier Tier Services Partner and Managed Service Provider (MSP).

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Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center

AWS Machine Learning

The Amazon Lex fulfillment AWS Lambda function retrieves the Talkdesk touchpoint ID and Talkdesk OAuth secrets from AWS Secrets Manager and initiates a request to Talkdesk Digital Connect using the Start a Conversation API. If the request to the Talkdesk API is successful, a Talkdesk conversation ID is returned to Amazon Lex.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon. With fully managed agents, you don’t have to worry about provisioning or managing infrastructure. The following diagram depicts the agent structure.

APIs 87