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Develop generative AI applications to improve teaching and learning experiences

AWS Machine Learning

Generative AI and natural language programming (NLP) models have great potential to enhance teaching and learning by generating personalized learning content and providing engaging learning experiences for students. For our example, a teacher inputs the Kids and Bicycle Safety guidelines from the United States Department of Transportation.

APIs 128
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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Revolutionize trip planning with Amazon Bedrock and Amazon Location Service

AWS Machine Learning

It’s like having your own personal travel agent whenever you need it. By using advanced AI technology and Amazon Location Service , the trip planner lets users translate inspiration into personalized travel itineraries. Amazon Bedrock is the place to start when building applications that will amaze and inspire your users.

APIs 108
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 134
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Enabling complex generative AI applications with Amazon Bedrock Agents

AWS Machine Learning

Using Amazon Bedrock Agents, a developer can quickly build a generative assistant to help answer this more complicated question by combining the LLM’s reasoning with additional tools and resources, such as natively integrated knowledge bases to propose personalized itineraries.

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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. We will start by using the SageMaker Studio UI and then by using APIs.

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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning

Examples of such information are personally identifiable information (PII), personal health information (PHI), and payment card industry (PCI) data. Both HTTP/2 and WebSockets streaming connections are established over Transport Layer Security (TLS), which is a widely accepted cryptographic protocol. We recommend using TLS 1.2