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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. EventBridge monitors status change events to automatically take actions with simple rules. API Gateway invokes a Lambda function to initiate model updates.

APIs 99
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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

A Lambda function called the Call Event Processor, fed by Kinesis Data Streams, processes and optionally enriches meeting metadata and transcription segments. The Call Event Processor integrates with the meeting assist services. The stacks take about 35–40 minutes to deploy. Authentication is provided by Amazon Cognito.

APIs 109
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Expedite your Genesys Cloud Amazon Lex bot design with the Amazon Lex automated chatbot designer

AWS Machine Learning

The solution workflow consists of the following steps: Genesys Cloud sends iterative transcripts events to your EventBridge event bus. Lambda receives the iterative transcripts from EventBridge, determines when a conversation is complete, and invokes the Transcript API within Genesys Cloud and drops the full transcript in an S3 bucket.

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AI in Customer Service: Chatbots and ChatGPT are just the Start

TechSee

Similarly, AI easily scales up and down to meet changing demands, eliminating long wait times and poor CX during mass service events or seasons. Like agents, the LLM must have visibility into system outage statuses and training on the latest agent guidance or best practices.

Chatbots 202
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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning

By using the Framework, you will learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. Set up regular game days to test workload and team responses to simulated events.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

The solution uses the following services: Amazon API Gateway is a fully managed service that makes it easy for developers to publish, maintain, monitor, and secure APIs at any scale. Purina’s solution is deployed as an API Gateway HTTP endpoint, which routes the requests to obtain pet attributes.

APIs 95