Category: Amazon Machine Learning

In this post, we delve into the process of building and deploying a sample application capable of generating multilingual descriptions for multiple images with a Streamlit UI, AWS Lambda powered with the Amazon Bedrock SDK, and AWS AppSync driven by the open source Generative AI CDK Constructs.

In this post, we provide insights and practical examples to help balance and optimize the prompt engineering workflow. We focus on advanced prompt techniques and best practices for the models provided in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies such as Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. With these prompting techniques, developers and researchers can harness the full capabilities of Amazon Bedrock, providing clear and concise communication while mitigating potential risks or undesirable outputs.

In this post, we show how you can recommend breaking news to a user using AWS AI/ML services. By taking advantage of the power of Amazon Personalize and Amazon Titan Text Embeddings on Amazon Bedrock, you can show articles to interested users within seconds of them being published.

In this blog post, we will show you how to implement tenant isolation using Amazon Bedrock agents within a multi-tenant environment. We’ll demonstrate this using a sample multi-tenant e-commerce application that provides a service for various tenants to create online stores. This application will use Amazon Bedrock agents to develop an AI assistant or chatbot capable of providing tenant-specific information, such as return policies and user-specific information like order counts and status updates.

In this post, we show you how to use Amazon Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.

In this post, we demonstrate how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and the AWS Cloud Development Kit (AWS CDK), enabling organizations to quickly set up a powerful question answering system.

Today, we are happy to announce the general availability of cross-region inference, a powerful feature allowing automatic cross-region inference routing for requests coming to Amazon Bedrock. This offers developers using on-demand inference mode, a seamless solution for managing optimal availability, performance, and resiliency while managing incoming traffic spikes of applications powered by Amazon Bedrock. By opting in, developers no longer have to spend time and effort predicting demand fluctuations.

In this post, we will harness the power of generative artificial intelligence (AI) and Amazon Bedrock to help organizations simplify and effectively manage remediations of AWS Security Hub control findings.

In this post, we discuss existing prompt-level threats and outline several security guardrails for mitigating prompt-level threats. For our example, we work with Anthropic Claude on Amazon Bedrock, implementing prompt templates that allow us to enforce guardrails against common security threats such as prompt injection. These templates are compatible with and can be modified for other LLMs.

In this post, we introduce the new Amazon Titan Text Premier model, specifically optimized for enterprise use cases, such as building Retrieval Augmented Generation (RAG) and agent-based applications. Such integrations enable advanced applications like building interactive AI assistants that use enterprise APIs and interact with your propriety documents.