Category: Learning Levels

In this post, we show how SageMaker HyperPod, and its new features introduced at AWS re:Invent 2024, is designed to meet the demands of modern AI workloads, offering a persistent and optimized cluster tailored for distributed training and accelerated inference at cloud scale and attractive price-performance.

This post focuses on RAG evaluation with HAQM Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. By the end of this post, you will understand how the latest HAQM Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.

In this post, we dive deeper into one of MaestroQA’s key features—conversation analytics, which helps support teams uncover customer concerns, address points of friction, adapt support workflows, and identify areas for coaching through the use of HAQM Bedrock. We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies.

In this blog post, we showcase a curated gallery of visuals generated by Nova Canvas—categorized by real-world use cases—from marketing and product visualization to concept art and design exploration. Each image is paired with the prompt and parameters that generated it, providing a practical starting point for your own AI-driven creativity. Whether you’re crafting specific types of images, optimizing workflows, or simply seeking inspiration, this guide will help you unlock the full potential of HAQM Nova Canvas.

In this post, we walk through how to build the FoodSavr solution (fictitious name used for the purposes of this post) using HAQM Rekognition Custom Labels to detect the ingredients and generate personalized recipes using Anthropic’s Claude 3.0 on HAQM Bedrock. We demonstrate an end-to-end architecture where a user can upload an image of their fridge, and using the ingredients found there (detected by HAQM Rekognition), the solution will give them a list of recipes (generated by HAQM Bedrock). The architecture also recognizes missing ingredients and provides the user with a list of nearby grocery stores.

Today, HAQM Web Services (AWS) announced the general availability of HAQM Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in HAQM Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in HAQM Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in HAQM Bedrock Knowledge Bases.

In this post, we explain how BMW uses generative AI to speed up the root cause analysis of incidents in complex and distributed systems in the cloud such as BMW’s Connected Vehicle backend serving 23 million vehicles. Read on to learn how the solution, collaboratively pioneered by AWS and BMW, uses HAQM Bedrock Agents and HAQM CloudWatch logs and metrics to find root causes quicker. This post is intended for cloud solution architects and developers interested in speeding up their incident workflows.

In this blog post, we will guide you through the process of integrating Chronos into HAQM SageMaker Pipeline using a synthetic dataset that simulates a sales forecasting scenario, unlocking accurate and efficient predictions with minimal data.

In this post, we discuss best practices for applying LLMs to generate ground truth for evaluating question-answering assistants with FMEval on an enterprise scale. FMEval is a comprehensive evaluation suite from HAQM SageMaker Clarify, and provides standardized implementations of metrics to assess quality and responsibility. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

HAQM Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search. In this post, we discuss using metadata filters with HAQM Bedrock Knowledge Bases.