Tag: HAQM Machine Learning

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

This post introduces a solution to reduce hallucinations in Large Language Models (LLMs) by implementing a verified semantic cache using HAQM Bedrock Knowledge Bases, which checks if user questions match curated and verified responses before generating new answers. The solution combines the flexibility of LLMs with reliable, verified answers to improve response accuracy, reduce latency, and lower costs while preventing potential misinformation in critical domains such as healthcare, finance, and legal services.

This post discusses the adoption and evaluation of HAQM Nova foundation models by Trellix, a leading company delivering cybersecurity’s broadest AI-powered platform to over 53,000 customers worldwide.

The post showcases how generative AI can be used to logic, reason, and orchestrate integrations using a fictitious business process. It demonstrates strategies and techniques for orchestrating HAQM Bedrock agents and action groups to seamlessly integrate generative AI with existing business systems, enabling efficient data access and unlocking the full potential of generative AI.

In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.

In this post, we’ll demonstrate how to configure an HAQM Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided by HAQM Q Business to query real-time data and take actions in ServiceNow.

This post demonstrates how to use HAQM Bedrock Agents, HAQM Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.

This post demonstrates a proactive approach for security vulnerability assessment of your accounts and workloads, using HAQM GuardDuty, HAQM Bedrock, and other AWS serverless technologies. This approach aims to identify potential vulnerabilities proactively and provide your users with timely alerts and recommendations, avoiding reactive escalations and other damages.

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 HAQM 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.

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 HAQM Personalize and HAQM Titan Text Embeddings on HAQM Bedrock, you can show articles to interested users within seconds of them being published.