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MLOps Pipeline Templates
End-to-end ML pipeline templates with feature engineering, training, evaluation, and deployment stages.
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📁 File Structure 4 files
mlops-pipeline-templates/
├── LICENSE
├── README.md
└── configs/
├── development.yaml
└── production.yaml
📖 Documentation Preview README excerpt
MLOps Pipeline Templates
End-to-end ML pipeline templates with feature engineering, training, evaluation, and deployment stages.
Contents
configs/development.yamlconfigs/production.yaml
Quick Start
1. Extract the ZIP archive
2. Review the README and documentation
3. Customize configuration files for your environment
4. Follow the setup guide for your specific use case
Requirements
- Python 3.10+ (for Python scripts)
- Relevant CLI tools for your platform
- Access to your target environment
License
MIT License — see LICENSE file.
Support
Questions or issues? Email megafolder122122@hotmail.com
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Part of [Ml Engineer](https://inity13.github.io/ml-engineer-toolkit/)
📄 Code Sample .yaml preview
configs/development.yaml
# MLOps Pipeline Templates — Development Configuration
environment: development
log_level: DEBUG
server:
host: "127.0.0.1"
port: 8080
workers: 1
timeout: 60
database:
host: "localhost"
port: 5432
name: "dev_db"
pool_size: 5
cache:
backend: redis
url: "redis://localhost:6379"
ttl: 60