Category: Learning Levels

In this post, we implemented secure fine-tuning jobs in HAQM Bedrock, which is crucial for protecting sensitive data and maintaining the integrity of your AI models. By following the best practices outlined in this post, including proper IAM role configuration, encryption at rest and in transit, and network isolation, you can significantly enhance the security posture of your fine-tuning processes.

In this post, we show you how to set up HAQM Lex for an omnichannel chatbot experience and HAQM Bedrock to be your secondary validation layer. This allows your customers to potentially provide out-of-band responses both at the intent and slot collection levels without having to be re-prompted, allowing for a seamless customer experience.

Today, we’re excited to announce multi-turn conversation with an agent node (preview), a powerful new capability in Flows. This new capability enhances the agent node functionality, enabling dynamic, back-and-forth conversations between users and flows, similar to a natural dialogue in a flow execution.

In this post, we show how you can integrate HAQM Lex with HAQM Bedrock Knowledge Bases and ServiceNow to provide 24/7 automated support and self-service options.

This post introduces HCLTech’s AutoWise Companion, a transformative generative AI solution designed to enhance customers’ vehicle purchasing journey. In this post, we analyze the current industry challenges and guide readers through the AutoWise Companion solution functional flow and architecture design using built-in AWS services and open source tools. Additionally, we discuss the design from security and responsible AI perspectives, demonstrating how you can apply this solution to a wider range of industry scenarios.

In this post, we show how you can use our enterprise graph machine learning (GML) framework GraphStorm to solve prediction challenges on large-scale complex networks inspired by our practices of exploring GML to mitigate the AWS backbone network congestion risk.

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.

In this post, we walk you through the process to build an automated mechanism using HAQM SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the HAQM SageMaker Model Registry for your customers to use it.

When building voice-enabled chatbots with HAQM Lex, one of the biggest challenges is accurately capturing user speech input for slot values. Transcription confidence scores can help ensure reliable slot filling. This blog post outlines strategies like progressive confirmation, adaptive re-prompting, and branching logic to create more robust slot filling experiences.

TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used HAQM SageMaker and HAQM Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.