Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)Artificial Intelligence This two-part series explores Flo Health’s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale.
This two-part series explores Flo Health’s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale. Read More
Beyond Prompting: The Power of Context EngineeringTowards Data Science Using ACE to create self-improving LLM workflows and structured playbooks
The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science.
Using ACE to create self-improving LLM workflows and structured playbooks
The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science. Read More
Detect and redact personally identifiable information using Amazon Bedrock Data Automation and GuardrailsArtificial Intelligence This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability.
This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability. Read More
Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing toolArtificial Intelligence Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint.
Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint. Read More
Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease PredictionMarkTechPost A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on
The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeared first on MarkTechPost.
A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on
The post Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction appeared first on MarkTechPost. Read More
Powerful Local AI Automations with n8n, MCP and OllamaKDnuggets The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems.
The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems. Read More
Retrieval for Time-Series: How Looking Back Improves ForecastsTowards Data Science Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can
The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science.
Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can
The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science. Read More
How to Improve the Performance of Visual Anomaly Detection ModelsTowards Data Science Apply the best methods from academia to get the most out of practical applications
The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science.
Apply the best methods from academia to get the most out of practical applications
The post How to Improve the Performance of Visual Anomaly Detection Models appeared first on Towards Data Science. Read More
“Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosisAI News AI advancements are changing the way we look at health and deal with health-related issues. According to a new nationwide study by Confused.com Life Insurance, three in five Brits now use AI to self-diagnose health conditions. Through various searches, like side effects of medical conditions, treatment options, and symptom checks, as much as 11% of
The post “Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosis appeared first on AI News.
AI advancements are changing the way we look at health and deal with health-related issues. According to a new nationwide study by Confused.com Life Insurance, three in five Brits now use AI to self-diagnose health conditions. Through various searches, like side effects of medical conditions, treatment options, and symptom checks, as much as 11% of
The post “Dr AI, am I healthy?” 59% of Brits rely on AI for self-diagnosis appeared first on AI News. Read More
10 Most Popular GitHub Repositories for Learning AIKDnuggets The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems.
The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems. Read More