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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machine learning. Choose the us-east-1 AWS Region from the top right corner. Choose Manage model access.

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Building a Scalable ML Pipeline and API in AWS

Dzone - DevOps

With rapid progress in the fields of machine learning (ML) and artificial intelligence (AI), it is important to deploy the AI/ML model efficiently in production environments. The architecture downstream ensures scalability, cost efficiency, and real-time access to applications.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning - AI

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. You can use AWS services such as Application Load Balancer to implement this approach.

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Discover, Protect and Respond with AWS and Prisma Cloud

Prisma Clud

Organizations are increasingly turning to cloud providers, like Amazon Web Services (AWS), to address these challenges and power their digital transformation initiatives. However, the vastness of AWS environments and the ease of spinning up new resources and services can lead to cloud sprawl and ongoing security risks.

AWS 105
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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This systematic approach leads to more reliable and standardized evaluations.

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Getting started with computer use in Amazon Bedrock Agents

AWS Machine Learning - AI

The computer use agent demo powered by Amazon Bedrock Agents provides the following benefits: Secure execution environment Execution of computer use tools in a sandbox environment with limited access to the AWS ecosystem and the web. Prerequisites AWS Command Line Interface (CLI), follow instructions here. Require Python 3.11

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