Remove features api-access
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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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Improving Content Moderation with Amazon Rekognition Bulk Analysis and Custom Moderation

AWS Machine Learning

It requires no machine learning (ML) expertise to use and we’re continually adding new computer vision features to the service. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon Simple Storage Service (Amazon S3). Graphic Violence L2 92.6%

APIs 103
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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 mapped to an AWS Lambda function and related API schema to perform API calls. Customers converse with the bot in natural language with multiple steps invoking external APIs to accomplish subtasks.

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Build a receipt and invoice processing pipeline with Amazon Textract

AWS Machine Learning

The next stage is the extraction phase, where you pass the collected invoices and receipts to the Amazon Textract AnalyzeExpense API to extract financially related relationships between text such as vendor name, invoice receipt date, order date, amount due, amount paid, and so on. It is available both as a synchronous or asynchronous API.

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Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy

AWS Machine Learning

In the recently released feature for Knowledge Bases for Amazon Bedrock, hybrid search , you can combine semantic search with keyword search. In this post, we discuss the new custom metadata filtering feature in Knowledge Bases for Amazon Bedrock, which you can use to improve search results by pre-filtering your retrievals from vector stores.

APIs 102
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Knowledge Bases for Amazon Bedrock now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

AWS Machine Learning

Access to additional data helps the model generate more relevant, context-specific, and accurate responses without retraining the FMs. In the following sections, we explain how you can use these features with either the AWS Management Console or SDK. In the following sections, we explain how you can use these features with the SDK.

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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. Features are inputs to ML models used during training and inference. Features are used repeatedly by multiple teams, and feature quality is critical to ensure a highly accurate model.