Category: *Post Types

In this post, we share some of the new innovations in SageMaker AI that can accelerate how you build and train AI models. These innovations include new observability capabilities in SageMaker HyperPod, the ability to deploy JumpStart models on HyperPod, remote connections to SageMaker AI from local development environments, and fully managed MLflow 3.0.

In this post, we explore how Amazon SageMaker now offers fully managed support for MLflow 3.0, streamlining AI experimentation and accelerating your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.

In this post, we announce Amazon SageMaker HyperPod support for deploying foundation models from SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. This new capability allows customers to train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model lifecycle.

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker Studio. In this post, we show you how to remotely connect your local VS Code to SageMaker Studio development environments to use your customized development environment while accessing Amazon SageMaker AI compute resources.

Rocket AI Agent is more than a digital assistant. It’s a reimagined approach to client engagement, powered by agentic AI. By combining Amazon Bedrock Agents with Rocket’s proprietary data and backend systems, Rocket has created a smarter, more scalable, and more human experience available 24/7, without the wait. This post explores how Rocket brought that vision to life using Amazon Bedrock Agents, powering a new era of AI-driven support that is consistently available, deeply personalized, and built to take action.

Amazon Nova Sonic is now integrated with LiveKit’s WebRTC framework, a widely used platform that enables developers to build real-time audio, video, and data communication applications. This integration makes it possible for developers to build conversational voice interfaces without needing to manage complex audio pipelines or signaling protocols. In this post, we explain how this integration works, how it addresses the historical challenges of voice-first applications, and some initial steps to start using this solution.

In this post, we announce general availability of Amazon EC2 P6e-GB200 UltraServers and P6-B200 instances, powered by NVIDIA Blackwell GPUs, designed for training and deploying the largest, most sophisticated AI models.

Amazon Q Business for Retail Intelligence is an AI-powered assistant designed to help retail businesses streamline operations, improve customer service, and enhance decision-making processes. This solution is specifically engineered to be scalable and adaptable to businesses of various sizes, helping them compete more effectively. In this post, we show how you can use Amazon Q Business for Retail Intelligence to transform your data into actionable insights.

The business team in Parcel Perform often needs access to data to answer questions related to merchants’ parcel deliveries, such as “Did we see a spike in delivery delays last week? If so, in which transit facilities were this observed, and what was the primary cause of the issue?” Previously, the data team had to manually form the query and run it to fetch the data. With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. In this post, we discuss how Parcel Perform incorporated generative AI, data storage, and data access through AWS services to make timely decisions.

In this post, we discuss how to make your Amazon Aurora PostgreSQL-Compatible Edition data available for natural language querying through Amazon Bedrock Knowledge Bases while maintaining data freshness.