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CI/CD Pipeline for ML
GitHub Actions workflows automating the full ML lifecycle: data validation, model training with performance gates, artifact storage, and multi-environment deployment.
Overview
Complete CI/CD pipeline automating the machine learning lifecycle from data validation to deployment.
Architecture
- GitHub Actions for workflow automation
- Data validation with Great Expectations
- Automated model training and evaluation
- Performance gates for deployment approval
- Multi-environment deployment strategy
Key Features
- Automated data quality checks
- Model performance gating
- Automated rollback on regression
- Dynamic README badges for metrics