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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. With this launch, customers can now seamlessly share and access ML models registered in SageMaker Model Registry between different AWS accounts.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers. For integration between services, we use API Gateway as an event trigger for our Lambda function, and DynamoDB as a highly scalable database to store our customer details.

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How Untold Studios empowers artists with an AI assistant built on Amazon Bedrock

AWS Machine Learning

The implementation uses Slacks event subscription API to process incoming messages and Slacks Web API to send responses. The incoming event from Slack is sent to an endpoint in API Gateway, and Slack expects a response in less than 3 seconds, otherwise the request fails. He has been helping customers at AWS for the past 4.5

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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

Frontend and API The CQ application offers a robust search interface specially crafted for call quality agents, equipping them with powerful auditing capabilities for call analysis. Additionally, the application offers backend dashboards tailored to MLOps functionalities, ensuring smooth monitoring and optimization of machine learning models.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning

Growing in the media and entertainment space, Veritone solves media management, broadcast content, and ad tracking issues. Amazon Transcribe The transcription for the entire video is generated using the StartTranscriptionJob API. The metadata generated for each video by the APIs is processed and stored with timestamps.

APIs 124
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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

APIs 116
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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning

This step produces an expanded report containing the model’s metrics. The AI/ML architecture for EarthSnap is designed around a series of AWS services: Sagemaker Pipeline runs using one of the methods mentioned above (CodeBuild, API, manual) that trains the model and produces artifacts and metrics.