Category: Generative AI

With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across […]

In this post, we show you how to build an internal SaaS layer to access foundation models with Amazon Bedrock in a multi-tenant (team) architecture. We specifically focus on usage and cost tracking per tenant and also controls such as usage throttling per tenant. We describe how the solution and Amazon Bedrock consumption plans map to the general SaaS journey framework. The code for the solution and an AWS Cloud Development Kit (AWS CDK) template is available in the GitHub repository.

Generative AI agents are a versatile and powerful tool for large enterprises. They can enhance operational efficiency, customer service, and decision-making while reducing costs and enabling innovation. These agents excel at automating a wide range of routine and repetitive tasks, such as data entry, customer support inquiries, and content generation. Moreover, they can orchestrate complex, […]

This post is co-written with Ilan Geller, Shuyu Yang and Richa Gupta from Accenture. Bringing innovative new pharmaceuticals drugs to market is a long and stringent process. Companies face complex regulations and extensive approval requirements from governing bodies like the US Food and Drug Administration (FDA). A key part of the submission process is authoring […]

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.