Languages
Features
ml-pipelinesbest-practicesreproducibilitydata-catalogmodular
Pros and Cons
Ventajas
- + Standardized project structure
- + Data Catalog for data management
- + Modular and reusable pipelines
- + Automatic documentation
- + Jupyter notebooks integration
- + Developed by McKinsey QuantumBlack
Desventajas
- - Initial learning curve
- - Structure can be rigid
- - Less flexible than pure scripts
- - Smaller community
- - Overhead for small projects
Casos de Uso
- Production ML projects
- Reproducible pipelines
- Data science team collaboration
- ML project standardization
- Structured experimentation
- MLOps with best practices