AWS Machine Learning Blog
How BQA streamlines education quality reporting using Amazon Bedrock
The Education and Training Quality Authority (BQA) plays a critical role in improving the quality of education and training services in the Kingdom Bahrain. BQA reviews the performance of all education and training institutions, including schools, universities, and vocational institutes, thereby promoting the professional advancement of the nation’s human capital. In this post, we explore how BQA used the power of Amazon Bedrock, Amazon SageMaker JumpStart, and other AWS services to streamline the overall reporting workflow.
Boosting team innovation, productivity, and knowledge sharing with Amazon Q Business – Web experience
This post shows how MuleSoft introduced a generative AI-powered assistant using Amazon Q Business to enhance their internal Cloud Central dashboard. This individualized portal shows assets owned, costs and usage, and well-architected recommendations to over 100 engineers.
Build an Amazon Bedrock based digital lending solution on AWS
In this post, we propose a solution using DigitalDhan, a generative AI-based solution to automate customer onboarding and digital lending. The proposed solution uses Amazon Bedrock Agents to automate services related to KYC verification, credit and risk assessment, and notification. Financial institutions can use this solution to help automate the customer onboarding, KYC verification, credit decisioning, credit underwriting, and notification processes.
Build AI-powered malware analysis using Amazon Bedrock with Deep Instinct
In this post, we explore how Deep Instinct’s generative AI-powered malware analysis tool, DIANNA, uses Amazon Bedrock to revolutionize cybersecurity by providing rapid, in-depth analysis of known and unknown threats, enhancing the capabilities of AWS System and Organization Controls (SOC) teams and addressing key challenges in the evolving threat landscape.
Email your conversations from Amazon Q
As organizations navigate the complexities of the digital realm, generative AI has emerged as a transformative force, empowering enterprises to enhance productivity, streamline workflows, and drive innovation. To maximize the value of insights generated by generative AI, it is crucial to provide simple ways for users to preserve and share these insights using commonly used tools such as email. This post explores how you can integrate Amazon Q Business with Amazon SES to email conversations to specified email addresses.
Unlock cost-effective AI inference using Amazon Bedrock serverless capabilities with an Amazon SageMaker trained model
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. In this post, I’ll show you how to use Amazon Bedrock—with its fully managed, on-demand API—with your Amazon SageMaker trained or fine-tuned model.
Align and monitor your Amazon Bedrock powered insurance assistance chatbot to responsible AI principles with AWS Audit Manager
Generative AI applications should be developed with adequate controls for steering the behavior of FMs. Responsible AI considerations such as privacy, security, safety, controllability, fairness, explainability, transparency and governance help ensure that AI systems are trustworthy. In this post, we demonstrate how to use the AWS generative AI best practices framework on AWS Audit Manager to evaluate this insurance claim agent from a responsible AI lens.
London Stock Exchange Group uses Amazon Q Business to enhance post-trade client services
In this blog post, we explore a client services agent assistant application developed by the London Stock Exchange Group (LSEG) using Amazon Q Business. We will discuss how Amazon Q Business saved time in generating answers, including summarizing documents, retrieving answers to complex Member enquiries, and combining information from different data sources (while providing in-text citations to the data sources used for each answer).
Evaluate large language models for your machine translation tasks on AWS
This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in Amazon Bedrock. It can help collect more data on the value of LLMs for your content translation use cases.
Parameta accelerates client email resolution with Amazon Bedrock Flows
In this post, we show you how Parameta used Amazon Bedrock Flows to transform their manual client email processing into an automated, intelligent workflow that reduced resolution times from weeks to days while maintaining high accuracy and operational control.