Category: Generative AI

In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face. In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried.

Resilience plays a pivotal role in the development of any workload, and generative AI workloads are no different. There are unique considerations when engineering generative AI workloads through a resilience lens. Understanding and prioritizing resilience is crucial for generative AI workloads to meet organizational availability and business continuity requirements. In this post, we discuss the […]

Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared. In this post, we describe how use generative AI to update and scale your data pipeline using Amazon […]

This post was co-written with Ricardo Perdigao, Solution Architecture Manager at Mendix, a Siemens business. Mendix, a Siemens business, offers the low-code platform with the vision and execution designed for today’s complex software development challenges. Since 2005, we’ve helped thousands of organizations worldwide reimagine how they develop applications with our platform’s cutting-edge capabilities. Mendix allows […]

This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs. We first delve into the vulnerabilities, threats, and risks that arise from the implementation, deployment, and use of LLM solutions, and provide guidance on how to start innovating with security in mind. We then discuss how building on a secure foundation is essential for generative AI. Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries.

In this post, we show you how to bring Amazon Q Business to users in Microsoft Teams. (If you use Slack, refer to Deploy a Slack gateway for Amazon Q Business) You’ll be able converse with Amazon Q Business using Teams direct messages (DMs) to ask questions and get answers based on company data, get help creating new content such as email drafts, summarize attached files, and perform tasks.

This post discusses how enterprises can build accurate, transparent, and secure generative AI applications while keeping full control over proprietary data. The proposed solution is a RAG pipeline using an AI-native technology stack, whose components are designed from the ground up with AI at their core, rather than having AI capabilities added as an afterthought. We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace.

Enterprises have access to massive amounts of data, much of which is difficult to discover because the data is unstructured. Conventional approaches to analyzing unstructured data use keyword or synonym matching. They don’t capture the full context of a document, making them less effective in dealing with unstructured data. In contrast, text embeddings use machine […]

With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a […]

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing […]