AWS Machine Learning Blog

In this post, we implemented secure fine-tuning jobs in Amazon 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 an example of a generative AI assistant application and demonstrate how to assess its security posture using the OWASP Top 10 for Large Language Model Applications, as well as how to apply mitigations for common threats.

In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.

In this post, we show you how to set up Amazon Lex for an omnichannel chatbot experience and Amazon 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 you an innovative solution to a challenge faced by security teams in highly regulated industries: the efficient security analysis of vast amounts of video recordings from Privileged Access Management (PAM) systems. We demonstrate how you can use Anthropic’s Claude 3 family of models and Amazon Bedrock to perform the complex task of analyzing video recordings of server console sessions and perform queries to highlight any potential security anomalies.

Accurately converting free text inputs into structured data is crucial for applications that involve data management and user interaction. In this post, we introduce a real business use case from Cato Networks that significantly improved user experience. By using Amazon Bedrock, we gained access to state-of-the-art generative language models with built-in support for JSON schemas and structured data.

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. We dive into Amazon SageMaker Canvas and explain how SageMaker Canvas can solve forecasting challenges for retail and consumer packaged goods (CPG) enterprises.

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

In this post, we show you how Kyndryl integrated Amazon Q Business with ServiceNow in a few simple steps. You will learn how to configure Amazon Q Business and ServiceNow, how to create a generative AI plugin for your ServiceNow incidents, and how to test and interact with ServiceNow using the Amazon Q Business web experience. This post will help you enhance your ServiceNow experience with Amazon Q Business and enjoy the benefits of a generative AI–powered interface.