Popular GitHub Repositories for Cloudwatch Alarm

The open-source AIOps and alert management platform

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

收集AIOPS（智能运维），时间序列，异常检测，关联分析，告警收敛，根因分析，数据挖掘，机器学习，深度学习的学习资源。欢迎star。Collect learning resources for AIOPS (Intelligent Operation and Maintenance), time series, anomaly detection, correlation analysis, alarm convergence, root cause analysis, data mining, machine learning, and deep learning. Welcome star.

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

A computer vision system made with the help of opencv that can automatically detect driver drowsiness in a real-time video stream and then play an alarm if the driver appears to be drowsy.

Desktop based log analysis for all your log sources

Robert, your own virtual assistant

[ACL 2024] Code for the paper "ALaRM: Align Language Models via Hierarchical Rewards Modeling"

@DeepLearning.AI Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It has helped me to develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

一个模型上下文协议 (MCP) 服务器，允许 AI 助手访问 AWS CloudWatch Logs 进行分析、搜索和关联分析。

This is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.

iOS alarm app w/ personal data powered by Core ML - Sound Classification

Teron AI - advanced AI assistant by Zeyan Ramzan. Offers chatbot, question answering, jokes, music, Wikipedia search, alarm setting and more. Dual voice options, YouTube video download and open for commits. Improve productivity and have fun with Teron AI.

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.

Dynamic and Chromatic Analysis for Fire Detection and Alarm Raising Using Real-Time Video Analysis

Matlab implementation of Neural Networks Results for Alarm-Warning Control System of mobile robot with five Ultrasonic sensors

Detect if the driver is feeling drowsy and sound an alarm on doing so to wake him/her up.

用于 AWS 成本管理器和 CloudWatch 日志的 MCP（多云平台）

Machine Learning on AWS using various methods/examples