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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 126
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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

The action is an API that the model can invoke from an allowed set of APIs. Action groups are tasks that the agent can perform autonomously. Action groups are mapped to an AWS Lambda function and related API schema to perform API calls. A set of actions comprise an action group.

APIs 87
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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

It’s straightforward to deploy in your AWS account. Prerequisites You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. Everything you need is provided as open source in our GitHub repo.

APIs 110
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Build well-architected IDP solutions with a custom lens – Part 2: Security

AWS Machine Learning

Design principles The Security Pillar encompasses the ability of an IDP solution to protect input documents, document processing systems, and output assets, taking advantage of AWS technologies to improve security while processing documents intelligently. based provider with AWS Identity and Access Management (IAM).

APIs 84
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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning

The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s. frameworks to restrict client access to your APIs. Enter a model group name.

APIs 77