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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 103
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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 112
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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 98
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Build well-architected IDP solutions with a custom lens – Part 4: Performance efficiency

AWS Machine Learning

The AWS Well-Architected Framework provides a systematic way for organizations to learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. This helps you avoid throttling limits on API calls due to polling the Get* APIs.

APIs 96
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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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Prevent account takeover at login with the new Account Takeover Insights model in Amazon Fraud Detector

AWS Machine Learning

Amazon Fraud Detector relies on specific models with tailored algorithms, enrichments, and feature transformations to detect fraudulent events across multiple use cases. The ATI model is trained using a dataset containing your business’s historical login events. Define the entity, event and event variables, and event label (optional).