AWS Machine Learning Blog
Amazon Bedrock Marketplace now includes NVIDIA models: Introducing NVIDIA Nemotron-4 NIM microservices
At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs). In this post, we discuss the advantages and capabilities of Amazon Bedrock Marketplace and Nemotron models, and how to get started.
Real value, real time: Production AI with Amazon SageMaker and Tecton
In this post, we discuss how Amazon SageMaker and Tecton work together to simplify the development and deployment of production-ready AI applications, particularly for real-time use cases like fraud detection. The integration enables faster time to value by abstracting away complex engineering tasks, allowing teams to focus on building features and use cases while providing a streamlined framework for both offline training and online serving of ML models.
Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models
In this post, we explore how to deploy AI models from SageMaker JumpStart and use them with Amazon Bedrock’s powerful features. Users can combine SageMaker JumpStart’s model hosting with Bedrock’s security and monitoring tools. We demonstrate this using the Gemma 2 9B Instruct model as an example, showing how to deploy it and use Bedrock’s advanced capabilities.
A guide to Amazon Bedrock Model Distillation (preview)
This post introduces the workflow of Amazon Bedrock Model Distillation. We first introduce the general concept of model distillation in Amazon Bedrock, and then focus on the important steps in model distillation, including setting up permissions, selecting the models, providing input dataset, commencing the model distillation jobs, and conducting evaluation and deployment of the student models after model distillation.
Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio
In this post, we’ll show how anyone in your company can use Amazon Bedrock IDE to quickly create a generative AI chat agent application that analyzes sales performance data. Through simple conversations, business teams can use the chat agent to extract valuable insights from both structured and unstructured data sources without writing code or managing complex data pipelines.
Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod
The integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution that provides data scientists and ML engineers with a comprehensive environment that supports the entire ML lifecycle, from development to deployment at scale. In this post, we walk you through the process of scaling your ML workloads using SageMaker Studio and SageMaker HyperPod.
Introducing Amazon Kendra GenAI Index – Enhanced semantic search and retrieval capabilities
Amazon has introduced the Amazon Kendra GenAI Index, a new offering designed to enhance semantic search and retrieval capabilities for enterprise AI applications. This index is optimized for Retrieval Augmented Generation (RAG) and intelligent search, allowing businesses to build more effective digital assistants and search experiences.
Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker
Today, we’re excited to announce that AI apps from AWS Partners are now available in SageMaker. You can now find, deploy, and use these AI apps privately and securely, all without leaving SageMaker AI, so you can develop performant AI models faster.
Query structured data from Amazon Q Business using Amazon QuickSight integration
In this post, we show how Amazon Q Business integrates with QuickSight to enable users to query both structured and unstructured data in a unified way. The integration allows users to connect to over 20 structured data sources like Amazon Redshift and PostgreSQL, while getting real-time answers with visualizations. Amazon Q Business combines information from structured sources through QuickSight with unstructured content to provide comprehensive answers to user queries.
Elevate customer experience by using the Amazon Q Business custom plugin for New Relic AI
The New Relic AI custom plugin for Amazon Q Business creates a unified solution that combines New Relic AI’s observability insights and recommendations and Amazon Q Business’s Retrieval Augmented Generation (RAG) capabilities, in and a natural language interface for ease of use. This post explores the use case, how this custom plugin works, how it can be enabled, and how it can help elevate customers’ digital experiences.