LEO is an open source tool to industrialise machine learning models
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Updated
Apr 11, 2020 - HTML
LEO is an open source tool to industrialise machine learning models
The documentation to the services in the Swisscom Application Cloud marketplace
A comprehensive, production-grade DevOps & Kubernetes learning path. Features multi-tier app deployment, GitOps (ArgoCD), CI/CD (Jenkins), AIOps (K8sGPT), Observability (Prometheus/Grafana/ELK), and Security (Trivy/SonarQube/Kyverno).
This project focuses on deploying a web application using Docker Swarm, including comprehensive logging, monitoring, and alerting systems. The infrastructure is fully automated with Vagrant, Ansible, and Terraform through Jenkins and GitLab CI/CD.
Backend example for implementing through Spring Boot and microservices.
project for sacco management
DevOps: CI/CD pipeline for Python weather web app
The project was built using HTML, CSS, JavaScript for the frontend, and MySQL for the backend. Below, I have outlined the key aspects of the project, including the code implementation, challenges encountered, and solutions provided.
Nginx Loggin using ELK Stack with gZip Compression and Caching
An Ansible role to configure logstash
Enterprise-grade Zero Trust IAM lab with automated JML workflows, periodic access reviews, and strict RBAC. Containerized using Keycloak (OIDC/SSO), OpenBao (dynamic secrets), Nginx, and ELK Stack for real-time identity threat detection.
An Ansible role to install and configure filebeat
An Ansible role to generate certificates for elasticsearch starting from a provided CA
Kibana Trafficlight Visualization Plugin
🛡️ SOC Tier 1 simulé — ELK Stack + Wazuh HIDS + TheHive SOAR | Simulation Metasploit + corrélation SIEM | Projet académique ESAIP
A dockerized application for performing sentiment analysis, tag recognition, and text summarization on YouTube videos.
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