A simple dashboard display for metrics gathered from a cf-hoover instance

RCIAM Metrics

Partner Metrics provides useful revenue metrics of your app, theme and affiliate revenue generated from the Shopify Partner Program.

PySODEvalToolkit: A Python-based Evaluation Toolbox for Salient Object Detection and Camouflaged Object Detection

Include this action for continuous code quality monitoring.

A Prometheus exporter and a REST API server to export metrics of compute units of resource managers like SLURM, Openstack, k8s, _etc_

GitHub action to generate metrics for a repository

For easy metric logging and visualization

How to increase visibility of a system using Grafana and RED metrics

Extract data from Excel report to convert to a Power BI data model using industry best practices to create a demo replacement customer retention report.

An easy-to-use, cross-platform measurement tool that pulls data out of CD pipelines and analysis the four key metrics for you.

Distributed monitoring system

A framework for evaluating Recommender Systems (EOSC Recommender System)

CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline

Visualization package for Fōrmulæ, in JavaScript

Exploring the depths of generative learning with a $\beta$-Variational Autoencoder ($\beta$-VAE) applied to the MNIST dataset for robust digit reconstruction and latent space analysis.

Primer proyecto de <Laboratoria> de la cohorte DEV013. Text-Analyzer iniciado el 7 de diciembre 2023 y culminado el 2 de enero de 2024

Tool to show software code quality metrics, measured by PHP Depend, in console and CI/CD

Distributed log monitoring solution leveraging Elasticsearch and Kibana, designed for scalability and enhanced system insights. Streamlines logging from multiple microservices, centralizes processing via Logstash, and provides comprehensive Kibana dashboards for granular metric visualization.

This GitHub repository contains a comprehensive analysis of the popular Iris dataset using various machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), and Random Forest. Additionally, it explores the impact of different data split ratios (80-10-10 vs. 60-20-20) on model performance.