{"id":25907909,"url":"https://github.com/jenniferopal/sensory_ai_project","last_synced_at":"2026-02-21T11:01:12.490Z","repository":{"id":280327235,"uuid":"941630979","full_name":"jenniferopal/sensory_ai_project","owner":"jenniferopal","description":"Building a production-grade microservices platform for processing and analysing sensory data in real-time, built with DevOps best practices.","archived":false,"fork":false,"pushed_at":"2025-03-02T18:41:16.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T19:32:12.609Z","etag":null,"topics":["ci-cd","devops","docker","fastapi","grafana","k8s","kubernetes","machine-learning","monitoring","python","terraform","yaml"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jenniferopal.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-02T18:31:46.000Z","updated_at":"2025-03-02T19:14:55.000Z","dependencies_parsed_at":"2025-03-02T19:42:22.268Z","dependency_job_id":null,"html_url":"https://github.com/jenniferopal/sensory_ai_project","commit_stats":null,"previous_names":["jenniferopal/sensory_ai_project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jenniferopal%2Fsensory_ai_project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jenniferopal%2Fsensory_ai_project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jenniferopal%2Fsensory_ai_project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jenniferopal%2Fsensory_ai_project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jenniferopal","download_url":"https://codeload.github.com/jenniferopal/sensory_ai_project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241622601,"owners_count":19992504,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ci-cd","devops","docker","fastapi","grafana","k8s","kubernetes","machine-learning","monitoring","python","terraform","yaml"],"created_at":"2025-03-03T07:15:38.812Z","updated_at":"2025-10-16T21:05:04.378Z","avatar_url":"https://github.com/jenniferopal.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SensorNet: ML-Powered Sensory Monitoring Platform\n\nA production-grade microservices platform for processing and analyzing sensory data in real-time, built with DevOps best practices.\n\n## Project Overview\n\nSensorNet demonstrates modern DevOps practices through a practical application that processes environmental sensory data. This project serves as both a learning experience and a portfolio showcase of DevOps skills including containerization, orchestration, CI/CD, and infrastructure as code.\n\nThe system collects environmental data (sound, light, movement) from sensors, processes it through ML pipelines, and provides real-time insights and recommendations through an API.\n\n## Technology Stack\n\n- **Container Orchestration**: Kubernetes\n- **CI/CD**: GitHub Actions\n- **Monitoring**: Prometheus \u0026 Grafana\n- **Infrastructure as Code**: Terraform\n- **Backend Services**: Python FastAPI\n- **ML Pipeline**: MLflow\n\n## Features\n\n- **Microservices Architecture**: Scalable, resilient services with clear separation of concerns\n- **Automated CI/CD Pipeline**: Continuous integration and deployment with GitHub Actions\n- **Infrastructure as Code**: Reproducible infrastructure with Terraform\n- **Observability**: Comprehensive monitoring with Prometheus and Grafana\n- **ML Integration**: Real-time data analysis with machine learning\n\n## Getting Started\n\n### Prerequisites\n\n- Docker Desktop with Kubernetes enabled\n- kubectl\n- Minikube (for local development)\n- Terraform (optional, for infrastructure deployment)\n\n### Quick Start\n\n1. Clone the repository\n   ```bash\n   git clone https://github.com/yourusername/sensornet.git\n   cd sensornet\n   ```\n\n2. Start a local Kubernetes cluster\n   ```bash\n   minikube start\n   ```\n\n3. Deploy the data collector service\n   ```bash\n   kubectl apply -f kubernetes/datacollector-pod.yaml\n   kubectl apply -f kubernetes/datacollector-service.yaml\n   ```\n\n4. Access the API\n   ```bash\n   kubectl port-forward service/datacollector-service 8080:8080\n   ```\n   Then visit http://localhost:8080/health\n\n### Development Workflow\n\nThis project follows a GitOps workflow:\n\n1. Make changes in a feature branch\n2. Open a pull request to trigger CI checks\n3. Upon approval and merge, CD pipeline deploys to the target environment\n\n## Project Structure\n\n```\nsensornet/\n├── .github/workflows/    # CI/CD pipeline definitions\n├── kubernetes/           # Kubernetes manifests\n├── services/             # Microservice source code\n│   ├── data-collector/   # Sensor data collection service\n│   └── ml-predictor/     # ML prediction service\n├── terraform/            # Infrastructure as code\n└── monitoring/           # Monitoring configurations\n```\n\n## Learning Path\n\nThis project was built incrementally, following a learn-by-doing approach:\n\n1. **Containerization**: Starting with Dockerizing a simple FastAPI service\n2. **Kubernetes Basics**: Deploying single pods, understanding YAML configuration\n3. **Service Networking**: Exposing and connecting services\n4. **Deployment Strategies**: Moving to Deployments for scalability\n5. **Observability**: Adding monitoring and logging\n6. **Infrastructure as Code**: Managing infrastructure with Terraform\n7. **CI/CD**: Implementing automated pipelines\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\n- Built as a portfolio project to demonstrate DevOps practices\n- Inspired by real-world sensory monitoring systems\n- Thanks to the open-source community for the amazing tools that make this possible. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjenniferopal%2Fsensory_ai_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjenniferopal%2Fsensory_ai_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjenniferopal%2Fsensory_ai_project/lists"}