ML Pipeline Workflow
Skill Verified ActiveBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
To provide a structured and comprehensive framework for designing, implementing, and automating end-to-end Machine Learning Operations (MLOps) pipelines.
Features
- End-to-end MLOps pipeline design
- Data preparation, validation, and feature engineering
- Model training orchestration and hyperparameter management
- Model validation and performance monitoring
- Automated deployment strategies and rollback mechanisms
Use Cases
- Building new ML pipelines from scratch
- Designing workflow orchestration for ML systems
- Implementing data -> model -> deployment automation
- Setting up reproducible training workflows
Non-Goals
- Developing specific ML algorithms
- Providing a runtime environment for model execution
- Managing cloud infrastructure directly (focus is on orchestration)
Workflow
- Define pipeline stages and dependencies
- Execute data preparation and validation
- Orchestrate model training and hyperparameter tuning
- Perform model validation and generate reports
- Automate model deployment and configure monitoring
- Implement rollback mechanisms and continuous monitoring
Practices
- Pipeline Design
- Data Management
- Model Operations
- Deployment Strategies
Installation
First, add the marketplace
/plugin marketplace add wshobson/agents/plugin install machine-learning-ops@claude-code-workflowsQuality Score
VerifiedTrust Signals
Similar Extensions
Project Session Manager
100Worktree-first dev environment manager for issues, PRs, and features with optional tmux sessions
Acp Router
100Route plain-language requests for Pi, Claude Code, Cursor, Copilot, OpenClaw ACP, OpenCode, Gemini CLI, Qwen, Kiro, Kimi, iFlow, Factory Droid, Kilocode, or explicit ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation. Codex chat binding defaults to the native Codex app-server plugin unless ACP is explicit or background spawn needs ACP.
SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Arize Evaluator
100Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Molfeat
99Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Build Feature Store
99Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.