Category: Analytics

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 demonstrate how to use SageMaker Unified Studio and AWS Identity and Access Management (IAM) to establish a robust permission framework for Amazon Bedrock models. We show how administrators can precisely manage which users and teams have access to specific models within a secure, collaborative environment. We guide you through creating granular permissions to control model access, with code examples for common enterprise governance scenarios.

In this post, we show how Bayer Crop Science manages large-scale data science operations by training models for their data analytics needs and maintaining high-quality code documentation to support developers. Through these solutions, Bayer Crop Science projects up to a 70% reduction in developer onboarding time and up to a 30% improvement in developer productivity.

In this post, we show how to build a fully deployable solution that processes video streams using OpenCV, Amazon Bedrock for contextual scene understanding and automated responses through Amazon Bedrock Agents. This solution extends the capabilities demonstrated in Automate chatbot for document and data retrieval using Amazon Bedrock Agents and Knowledge Bases, which discussed using Amazon Bedrock Agents for document and data retrieval. In this post, we apply Amazon Bedrock Agents to real-time video analysis and event monitoring.

In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.

In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application.

In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower.

In this post, we demonstrate how you can use SageMaker Unified Studio to create complex AI workflows using Amazon Bedrock Flows.

In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.