AWS Machine Learning Blog
Vision use cases with Llama 3.2 11B and 90B models from Meta
This is the first time that the Llama models from Meta have been released with vision capabilities. These new capabilities expand the usability of Llama models from their traditional text-only applications. In this post, we demonstrate how you can use Llama 3.2 11B and 90B models for a variety of vision-based use cases.
How generative AI is transforming legal tech with AWS
Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. In this post, we share how legal tech professionals can build solutions for different use cases with generative AI on AWS.
Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases
In this post, we show you how DoorDash built a generative AI agent using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases to provide a low-latency, self-service experience for their delivery workers.
Migrating to Amazon SageMaker: Karini AI Cut Costs by 23%
In this post, we share how Karini AI’s migration of vector embedding models from Kubernetes to Amazon SageMaker endpoints improved concurrency by 30% and saved over 23% in infrastructure costs.
Harnessing the power of AI to drive equitable climate solutions: The AI for Equity Challenge
The International Research Centre on Artificial Intelligence (IRCAI), Zindi, and Amazon Web Services (AWS) are proud to announce the launch of the “AI for Equity Challenge: Climate Action, Gender, and Health”—a global virtual competition aimed at empowering organizations to use advanced AI and cloud technologies to drive real-world impact with a focus on benefitting vulnerable populations around the world.
Enhancing Just Walk Out technology with multi-modal AI
In this post, we showcase the latest generation of Just Walk Out technology by Amazon, powered by a multi-modal foundation model (FM). We designed this multi-modal FM for physical stores using a transformer-based architecture similar to that underlying many generative artificial intelligence (AI) applications.
Generate synthetic data for evaluating RAG systems using Amazon Bedrock
In this post, we explain how to use Anthropic Claude on Amazon Bedrock to generate synthetic data for evaluating your RAG system.
Making traffic lights more efficient with Amazon Rekognition
In this blog post, we show you how Amazon Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.
Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio
In this post, we present a real-world use case analyzing the Diabetes 130-US hospitals dataset to develop an ML model that predicts the likelihood of readmission after discharge.
Govern generative AI in the enterprise with Amazon SageMaker Canvas
In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.