{"id":29174914,"url":"https://github.com/nickemma/sentiment_analysis_service","last_synced_at":"2026-04-09T15:42:57.832Z","repository":{"id":302054425,"uuid":"1011060635","full_name":"nickemma/sentiment_analysis_service","owner":"nickemma","description":"This basic MLOps project demonstrates the core concepts of model lifecycle management across your preferred technology stack!","archived":false,"fork":false,"pushed_at":"2025-06-30T10:22:11.000Z","size":1,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-30T10:41:12.969Z","etag":null,"topics":["aws","docker-compose","dynamodb","fastapi","gin-gonic","golang","java","mlops","postgresql","python","reactjs","spring-boot","tailwindcss"],"latest_commit_sha":null,"homepage":"","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nickemma.png","metadata":{"files":{"readme":"ReadME.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2025-06-30T08:40:33.000Z","updated_at":"2025-06-30T10:22:14.000Z","dependencies_parsed_at":"2025-06-30T10:41:17.131Z","dependency_job_id":"800345b8-e352-4b69-938d-2a03352e8749","html_url":"https://github.com/nickemma/sentiment_analysis_service","commit_stats":null,"previous_names":["nickemma/sentiment_analysis_service"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nickemma/sentiment_analysis_service","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickemma%2Fsentiment_analysis_service","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickemma%2Fsentiment_analysis_service/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickemma%2Fsentiment_analysis_service/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickemma%2Fsentiment_analysis_service/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nickemma","download_url":"https://codeload.github.com/nickemma/sentiment_analysis_service/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickemma%2Fsentiment_analysis_service/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262988800,"owners_count":23395684,"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":["aws","docker-compose","dynamodb","fastapi","gin-gonic","golang","java","mlops","postgresql","python","reactjs","spring-boot","tailwindcss"],"created_at":"2025-07-01T15:25:46.133Z","updated_at":"2025-12-30T18:59:39.154Z","avatar_url":"https://github.com/nickemma.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Simple MLOps Project - Sentiment Analysis Service\n\n### 🚀 Project Overview\nA comprehensive MLOps pipeline for sentiment analysis demonstrating modern microservices architecture with model training, serving, and monitoring across multiple programming languages and technologies.\n\n## 🏗️ Architecture\n┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐\n│   Python/FastAPI│    │  Java/Spring    │    │   Go/Gin        │\n│   ML Training   │───▶│  Model Serving  │───▶│   Monitoring    │\n│   \u0026 Validation  │    │   \u0026 Inference   │    │   \u0026 Logging     │\n└─────────────────┘    └─────────────────┘    └─────────────────┘\n         │                        │                        │\n         ▼                        ▼                        ▼\n┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐\n│   PostgreSQL    │    │    DynamoDB     │    │   DynamoDB      │\n│   Training Data │    │  Model Metadata │    │   Predictions   │\n│   \u0026 Features    │    │   \u0026 Versions    │    │   \u0026 Metrics     │\n└─────────────────┘    └─────────────────┘    └─────────────────┘\n\n### 🛠️ Technology Stack\n\n- ML Training Service: Python 3.9+ with FastAPI, scikit-learn, PostgreSQL\n- Model Serving Service: Java 17 with Spring Boot, DynamoDB\n- Monitoring Service: Go 1.19+ with Gin framework, DynamoDB\n- Databases: PostgreSQL for training data, DynamoDB for metadata and predictions\n- Containerization: Docker \u0026 Docker Compose\n- ML Libraries: scikit-learn, joblib, pandas\n\n### 🚀 Quick Start\n#### Prerequisites\n\n- Docker and Docker Compose\n- Git\n\n### Installation \u0026 Setup\n1. Clone the repository\n```bash\ngit clone https://github.com/yourusername/simple-mlops.git\ncd simple-mlops\n```\n2. Start all services\n```bash \nbashdocker-compose up -d\n```\n\n3. Verify services are running\n```bash \n#Check health endpoints\ncurl http://localhost:8001/health  # ML Training\ncurl http://localhost:8002/health  # Model Serving  \ncurl http://localhost:8003/health  # Monitoring\n```\n\n4. Train a model\n```bash\ncurl -X POST http://localhost:8001/train \\\n     -H \"Content-Type: application/json\" \\\n     -d '{\"retrain\": false}'\n```\n\n5. Make predictions\n```bash\ncurl -X POST http://localhost:8002/api/v1/predict \\\n     -H \"Content-Type: application/json\" \\\n     -d '{\"text\": \"This movie is amazing!\"}'\n```\n\n6. View monitoring metrics\n```bash\ncurl http://localhost:8003/metrics/predictions\ncurl http://localhost:8003/metrics/model\n```\n\n### 📊 API Endpoints\n### ML Training Service (Port 8001)\n\n- **POST /train** - Train new model\n- **GET /model/info** - Get current model information\n- **GET /health** - Health check\n\n### Model Serving Service (Port 8002)\n\n- **POST /api/v1/predict** - Make sentiment predictions\n- **GET /api/v1/model/status** - Get model status\n- **GET /health** - Health check\n\n### Monitoring Service (Port 8003)\n\n- **GET /metrics/predictions** - Get prediction metrics\n- **GET /metrics/model** - Get model performance metrics\n- **POST /metrics/alert** - Create monitoring alert\n- **GET /health** - Health check\n\n### 📈 Features\n\n- Multi-language Architecture: Python for ML, Java for serving, Go for monitoring\n- Scalable Design: Microservices architecture with Docker containers\n- Model Versioning: Track model versions and metadata in DynamoDB\n- Real-time Monitoring: Track predictions, model performance, and system health\n- Data Pipeline: PostgreSQL for training data, DynamoDB for operational data\n- Health Checks: Comprehensive health monitoring across all services\n- RESTful APIs: Clean API design with proper HTTP status codes and JSON responses\n\n### 🔄 Development Workflow\n\n- Data Preparation: Load training data into PostgreSQL\n- Model Training: Use ML Training service to train sentiment analysis models\n- Model Deployment: Models are automatically saved and versioned\n- Inference: Model Serving service provides real-time predictions\n- Monitoring: Track model performance and system metrics\n\n### 🛡️ Production Considerations\n\n- Add authentication and authorization\n- Implement proper logging and error handling\n- Add model validation and A/B testing\n- Set up CI/CD pipelines\n- Configure production databases (RDS, DynamoDB)\n- Add load balancing and auto-scaling\n- Implement monitoring and alerting (Prometheus, Grafana)\n- Add data validation and drift detection\n\n### 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch (git checkout -b feature/amazing-feature)\n3. Commit your changes (git commit -m 'Add amazing feature')\n4. Push to the branch (git push origin feature/amazing-feature)\n5. Open a Pull Request\n\n### 📄 License\nThis project is licensed under the MIT [License] - see the LICENSE file for details.\n\n### 🙋‍♂️ Support\nFor questions and support, please open an issue in the GitHub repository or contact the maintainers.\n\nBuilt with ❤️ using Python, Java, Go, and modern MLOps practices","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickemma%2Fsentiment_analysis_service","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnickemma%2Fsentiment_analysis_service","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickemma%2Fsentiment_analysis_service/lists"}