domains / families / research / ml-pipeline-guide

ml-pipeline-guide

separate

Build and deploy reproducible production ML pipelines for research

When to use

• data ingestion
• feature engineering
• model training
• evaluation
• experiment tracking
• artifact management
• reproducible research workflows
• ablation studies

When not to use

• deployment at scale

Skill page

Source