Category: Analytics

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by HAQM Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.

In this post, we demonstrate how OfferUp transformed its foundational search architecture using HAQM Titan Multimodal Embeddings and OpenSearch Service, significantly increasing user engagement, improving search quality and offering users the ability to search with both text and images. OfferUp selected HAQM Titan Multimodal Embeddings and HAQM OpenSearch Service for their fully managed capabilities, enabling the development of a robust multimodal search solution with high accuracy and a faster time to market for search and recommendation use cases.

HAQM Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and HAQM through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. In this post, I’ll show you how to use HAQM Bedrock—with its fully managed, on-demand API—with your HAQM SageMaker trained or fine-tuned model.

This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in HAQM Bedrock. It can help collect more data on the value of LLMs for your content translation use cases.

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using […]

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.

In this post, we show how HAQM 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 HAQM Redshift and PostgreSQL, while getting real-time answers with visualizations. HAQM Q Business combines information from structured sources through QuickSight with unstructured content to provide comprehensive answers to user queries.

This post presents a strategy for optimizing LLM-based applications. Given the increasing need for efficient and cost-effective AI solutions, we present a serverless read-through caching blueprint that uses repeated data patterns. With this cache, developers can effectively save and access similar prompts, thereby enhancing their systems’ efficiency and response times.

Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.