Category: Learning Levels

This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without the need for data movement. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. We highlight the process of using Amazon Athena Federated Query to extract data from GCP BigQuery, using Amazon SageMaker Data Wrangler to perform data preparation, and then using the prepared data to build ML models within Amazon SageMaker Canvas, a no-code ML interface.

In this post, we will explore building a reusable RAG data pipeline on LangChain—an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless. The end solution is a reference architecture for scalable RAG indexing and deployment.

In this post, we cover the core concepts behind RAG architectures and discuss strategies for evaluating RAG performance, both quantitatively through metrics and qualitatively by analyzing individual outputs. We outline several practical tips for improving text retrieval, including using hybrid search techniques, enhancing context through data preprocessing, and rewriting queries for better relevance.

In this post, we demonstrate how to deploy a contextual AI assistant. We build a solution which provides users with a familiar and convenient interface using Amazon Bedrock Knowledge Bases, Amazon Lex, and Amazon Connect, with WhatsApp as the channel.

In this post, we explore how organizations can cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. We discuss how these powerful tools enable organizations to optimize compute resources and reduce the complexity of model training and fine-tuning. We explore how you can make an informed decision about which Amazon SageMaker service is most applicable to your business needs and requirements.

In this post, we explore a sample solution that you can use to deploy an image editing application by using AWS serverless services and generative AI services. We use Amazon Bedrock and an Amazon Titan FM that allow you to edit images by using prompts.

In this post, we dive into the architectural considerations and development lifecycle practices that can help you build robust, scalable, and secure intelligent agents.

In this post, we discuss how you can use AWS HealthScribe with Amazon Q Business to create a chatbot to quickly gain insights into patient clinician conversations.

In this post, we explore three scenarios demonstrating the versatility of integrating Amazon EFS with SageMaker Studio. These scenarios highlight how Amazon EFS can provide a scalable, secure, and collaborative data storage solution for data science teams.

In this post, we show you how to use Amazon Transcribe to get near real-time transcriptions of calls sent to Amazon Bedrock for summarization and sensitive data redaction. We’ll walk through an architecture that uses AWS Step Functions to orchestrate the process, providing seamless integration and efficient processing