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Create an end-to-end serverless digital assistant for semantic search with Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes a wide range of foundation models (FMs) available though an API without having to manage any infrastructure. Amazon API Gateway and AWS Lambda to create an API with an authentication layer and integrate with Amazon Bedrock. An API created with Amazon API Gateway.

APIs 129
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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

At the forefront of this evolution sits Amazon Bedrock , a fully managed service that makes high-performing foundation models (FMs) from Amazon and other leading AI companies available through an API. System integration – Agents make API calls to integrated company systems to run specific actions.

APIs 139
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Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

AWS Machine Learning

This text-to-video API generates high-quality, realistic videos quickly from text and images. Customizable environment – SageMaker HyperPod offers the flexibility to customize your cluster environment using lifecycle scripts. Video generation has become the latest frontier in AI research, following the success of text-to-image models.

Scripts 121
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Amazon SageMaker Domain in VPC only mode to support SageMaker Studio with auto shutdown Lifecycle Configuration and SageMaker Canvas with Terraform

AWS Machine Learning

You must also associate a security group for your VPC with these endpoints to allow all inbound traffic from port 443: SageMaker API: com.amazonaws.region.sagemaker.api. This is required to communicate with the SageMaker API. SageMaker runtime: com.amazonaws.region.sagemaker.runtime.

Scripts 122
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Continuous integration and continuous delivery (CI/CD) pipeline – Using the customer’s GitHub repository enabled code versioning and automated scripts to launch pipeline deployment whenever new versions of the code are committed. Wipro has used the input filter and join functionality of SageMaker batch transformation API.

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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning

Run your DLC container with a model training script to fine-tune the RoBERTa model. After model training is complete, package the saved model, inference scripts, and a few metadata files into a tar file that SageMaker inference can use and upload the model package to an Amazon Simple Storage Service (Amazon S3) bucket.

Scripts 82
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Safe image generation and diffusion models with Amazon AI content moderation services

AWS Machine Learning

Solution overview Amazon Rekognition and Amazon Comprehend are managed AI services that provide pre-trained and customizable ML models via an API interface, eliminating the need for machine learning (ML) expertise. The RESTful API will return the generated image and the moderation warnings to the client if unsafe information is detected.

APIs 103