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

AWS Machine Learning Blog

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. The Python application uses the Streamlit library to provide a user-friendly interface for interacting with a generative AI model.

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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

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By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. You need the following prerequisite resources: An AWS account.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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Accelerating ML experimentation with enhanced security: AWS PrivateLink support for Amazon SageMaker with MLflow

AWS Machine Learning Blog

It simplifies the often complex and time-consuming tasks involved in setting up and managing an MLflow environment, allowing ML administrators to quickly establish secure and scalable MLflow environments on AWS. For example, you can give users access permission to download popular packages and customize the development environment.

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Automate invoice processing with Streamlit and Amazon Bedrock

AWS Machine Learning Blog

Streamlit is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. Install Python 3.7

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Reduce conversational AI response time through inference at the edge with AWS Local Zones

AWS Machine Learning Blog

Hybrid architecture with AWS Local Zones To minimize the impact of network latency on TTFT for users regardless of their locations, a hybrid architecture can be implemented by extending AWS services from commercial Regions to edge locations closer to end users. Next, create a subnet inside each Local Zone. Amazon Linux 2).

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AWS Inferentia and AWS Trainium deliver lowest cost to deploy Llama 3 models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

Today, we’re excited to announce the availability of Meta Llama 3 inference on AWS Trainium and AWS Inferentia based instances in Amazon SageMaker JumpStart. In this post, we demonstrate how easy it is to deploy Llama 3 on AWS Trainium and AWS Inferentia based instances in SageMaker JumpStart.

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