AWS Machine Learning Blog

Category: Advanced (300)

From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 1

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.

Use Amazon SageMaker Studio with a custom file system in Amazon EFS

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.

Improve public speaking skills using a generative AI-based virtual assistant with Amazon Bedrock

In this post, we present an Amazon Bedrock powered virtual assistant that can transcribe presentation audio and examine it for language use, grammatical errors, filler words, and repetition of words and sentences to provide recommendations as well as suggest a curated version of the speech to elevate the presentation.

Introducing SageMaker Core: A new object-oriented Python SDK for Amazon SageMaker

Introducing SageMaker Core: A new object-oriented Python SDK for Amazon SageMaker

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

Improve LLM application robustness with Amazon Bedrock Guardrails and Amazon Bedrock Agents

In this post, we demonstrate how Amazon Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with Amazon Bedrock Agents.

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Automate user on-boarding for financial services with a digital assistant powered by Amazon Bedrock

In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.

Govern generative AI in the enterprise with Amazon SageMaker Canvas

Govern generative AI in the enterprise with Amazon SageMaker Canvas

In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.

Build a generative AI assistant to enhance employee experience using Amazon Q Business

Build a generative AI assistant to enhance employee experience using Amazon Q Business

In this blog post, we explore how you can use Amazon Q Business to build generative AI assistants that enhance employee experience and boost productivity. Amazon Q Business seamlessly integrates with internal data sources, knowledge bases, and productivity tools to equip your workforce with instant access to information, automated tasks, and personalized support.