AWS Machine Learning Blog

Tag: AI/ML

Unlock organizational wisdom using voice-driven knowledge capture with Amazon Transcribe and Amazon Bedrock

This post introduces an innovative voice-based application workflow that harnesses the power of Amazon Bedrock, Amazon Transcribe, and React to systematically capture and document institutional knowledge through voice recordings from experienced staff members. Our solution uses Amazon Transcribe for real-time speech-to-text conversion, enabling accurate and immediate documentation of spoken knowledge. We then use generative AI, powered by Amazon Bedrock, to analyze and summarize the transcribed content, extracting key insights and generating comprehensive documentation.

Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex

Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. When you activate this feature, all resources, versions, and aliases associated after activation will be synchronized across the chosen Regions. With Global Resiliency, the replicated bot resources and aliases in the […]

Build a video insights and summarization engine using generative AI with Amazon Bedrock

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime) to a centralized video insights and summarization engine. This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. The solution notes the logged actions per individual and provides suggested actions for the uploader. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

Create a generative AI-based application builder assistant using Amazon Bedrock Agents

Create a generative AI-based application builder assistant using Amazon Bedrock Agents

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.

Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. This example not only highlights the efficiency of SageMaker, but also its impact how geospatial ML can be used to monitor the environment for sustainability and conservation purposes.

Improve LLM application robustness with Amazon Bedrock Guardrails and Amazon Bedrock Agents

In this post, we demonstrate how Amazon Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with Amazon Bedrock Agents.

How generative AI is transforming legal tech with AWS

How generative AI is transforming legal tech with AWS

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. In this post, we share how legal tech professionals can build solutions for different use cases with generative AI on AWS.

Govern generative AI in the enterprise with Amazon SageMaker Canvas

Govern generative AI in the enterprise with Amazon SageMaker Canvas

In this post, we analyze strategies for governing access to Amazon Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use Amazon Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.