{"aiPlatform":"claude-code@2025.06","category":"data-analysis","commandName":"/ml-pipeline","content":"---\nname: Machine Learning Pipeline\ndescription: Design and implement a complete ML pipeline with data ingestion, feature engineering, model training, and deployment\nallowed_tools:\n  - memory          # For storing ML experiment results and model configurations  \n  - filesystem      # For implementing pipeline code, model artifacts, and data processing\n  - sqlite          # For data analysis and feature store operations\ntags:\n  - machine-learning\n  - data-pipeline\n  - mlops\n  - model-training\n  - deployment\n  - monitoring\n  - workflow\ncategory: development\nversion: 2.0.0\nauthor: AI Commands Team\n---\n\n# Machine Learning Pipeline\n\nDesign and implement a complete ML pipeline for: $ARGUMENTS\n\nCreate a production-ready pipeline including:\n\n1. **Data Ingestion**:\n   - Multiple data source connectors\n   - Schema validation with Pydantic\n   - Data versioning strategy\n   - Incremental loading capabilities\n\n2. **Feature Engineering**:\n   - Feature transformation pipeline\n   - Feature store integration\n   - Statistical validation\n   - Handling missing data and outliers\n\n3. **Model Training**:\n   - Experiment tracking (MLflow/W&B)\n   - Hyperparameter optimization\n   - Cross-validation strategy\n   - Model versioning\n\n4. **Model Evaluation**:\n   - Comprehensive metrics\n   - A/B testing framework\n   - Bias detection\n   - Performance monitoring\n\n5. **Deployment**:\n   - Model serving API\n   - Batch/stream prediction\n   - Model registry\n   - Rollback capabilities\n\n6. **Monitoring**:\n   - Data drift detection\n   - Model performance tracking\n   - Alert system\n   - Retraining triggers\n\nInclude error handling, logging, and make it cloud-agnostic. Use modern tools like DVC, MLflow, or similar. Ensure reproducibility and scalability.","contentHash":"80ffede51fbbbd8a5ecb5dd1e46955df15de7829c57fa900d240c06229a18fb1","copies":0,"createdAt":"2025-08-12T16:09:33.045Z","description":"Creates ML pipelines with data and ML engineering subagents","github":{"repoUrl":"https://github.com/Commands-com/commands","lastSyncDirection":"from-github","metadata":{"importedFrom":"github_repository","repoPrivate":false,"repoDefaultBranch":"main","connectedAt":"2025-08-12T16:09:33.045Z"},"importedAt":"2025-08-12T16:09:33.045Z","lastSyncAt":"2025-08-17T17:57:48.631Z","fileMapping":{"license":null,"readme":null,"assets":[],"mainFile":"workflows/ml-pipeline.md"},"selectedCommand":"ml-pipeline","fileShas":{"mainFile":"2e9d72b76421560a355424eb740f5453d22ea99a","yamlPath":"313647b1fb381389da33b7913e95baf617c4b392"},"branch":"main","connectionType":"commands_yaml","connected":true,"lastSyncCommit":"01591bc061d236bde47bf23b0f47e8afcf1a5144","importSource":"repository_import","installationId":"69232615","syncStatus":"synced"},"githubRepoUrl":"https://github.com/Commands-com/commands","id":"e2ab0232-8202-49f5-b022-7760832579ac","inputParameters":[{"defaultValue":"pytorch","name":"ml_framework","options":["pytorch","tensorflow","scikit-learn","huggingface","jax"],"description":"Machine learning framework to use","label":"ML Framework","type":"select","required":false},{"name":"pipeline_type","options":["training","inference","feature-engineering","end-to-end","streaming"],"description":"Type of ML pipeline to create","label":"Pipeline Type","type":"select","required":false}],"instructions":"Creates ML pipelines with data and ML engineering subagents","likes":0,"mcp_search_content":"","organizationUsername":"commands-com","price":"free","search_content":"ml pipeline creates ml pipelines with data and ml engineering subagents /ml-pipeline data-analysis claude-code@2025.06","title":"ML Pipeline","type":"command","updatedAt":"2025-08-17T17:57:48.631Z","userId":"W0V8NAw5AhWRwcuwSoFLOi1Yem83","visibility":"public","name":"ml-pipeline","userInteraction":{"userHasStarred":false}}