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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning Blog

SageMaker Unified Studio combines various AWS services, including Amazon Bedrock , Amazon SageMaker , Amazon Redshift , Amazon Glue , Amazon Athena , and Amazon Managed Workflows for Apache Airflow (MWAA) , into a comprehensive data and AI development platform. Navigate to the AWS Secrets Manager console and find the secret -api-keys.

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Generative AI roadshow in North America with AWS and Hugging Face

AWS Machine Learning Blog

In 2023, AWS announced an expanded collaboration with Hugging Face to accelerate our customers’ generative artificial intelligence (AI) journey. Hugging Face, founded in 2016, is the premier AI platform with over 500,000 open source models and more than 100,000 datasets. Reach out to your AWS Account Manager or submit your request.

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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

Flipboard

This post describes a pattern that AWS and Cisco teams have developed and deployed that is viable at scale and addresses a broad set of challenging enterprise use cases. AWS solution architecture In this section, we illustrate how you might implement the architecture on AWS.

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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

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Manage your Amazon Lex bot via AWS CloudFormation templates

AWS Machine Learning Blog

It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Version control – With AWS CloudFormation, you can use version control systems like Git to manage your CloudFormation templates.

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