Category: Learning Levels

In this post, we explore a solution for implementing load balancing across login nodes in Slurm-based HyperPod clusters. By distributing user activity evenly across all available nodes, this approach provides more consistent performance, better resource utilization, and a smoother experience for all users. We guide you through the setup process, providing practical steps to achieve effective load balancing in your HyperPod clusters.

In this post, we demonstrate how we innovated to build a Retrieval Augmented Generation (RAG) application with agentic workflow and a knowledge base on HAQM Bedrock. We implemented the RAG pipeline in a Slack chat-based assistant to empower the HAQM Twitch ads sales team to move quickly on new sales opportunities.

In April 2024, we launched our AI sales assistant, which we call Field Advisor, making it available to AWS employees in the Sales, Marketing, and Global Services organization, powered by HAQM Q Business. Since that time, thousands of active users have asked hundreds of thousands of questions through Field Advisor, which we have embedded in our customer relationship management (CRM) system, as well as through a Slack application.

In Parts 1 and 2 of this series, we explored ways to use the power of multimodal FMs such as HAQM Titan Multimodal Embeddings, HAQM Titan Text Embeddings, and Anthropic’s Claude 3 Sonnet. In this post, we compared the approaches from an accuracy and pricing perspective.

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in HAQM SageMaker, users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. In the initial stages of an ML […]

In this post, we explore how HAQM SageMaker HyperPod training plans accelerate compute resource procurement for machine learning workloads. We guide you through a step-by-step implementation on how you can use the AWS CLI or the AWS Management Console to find, review, and create optimal training plans for your specific compute and timeline needs. We further guide you through using the training plan to submit SageMaker training jobs or create SageMaker HyperPod clusters.

In this post, we discuss how HAQM 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.

In this post, we’ll show how anyone in your company can use HAQM Bedrock in SageMaker Unified Studio 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.

The integration of HAQM SageMaker Studio and HAQM 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.

Chronos models are available for HAQM SageMaker customers through AutoGluon-TimeSeries and HAQM SageMaker JumpStart. In this post, we introduce Chronos-Bolt, our latest FM for forecasting that has been integrated into AutoGluon-TimeSeries.