ML Pipeline Expert
技能 已验证 活跃Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.
To enable users to build robust, reproducible, and scalable production-grade ML pipelines by providing expert guidance, code templates, and best practices for MLOps tooling and workflows.
功能
- Designs ML pipeline architecture
- Configures experiment tracking (MLflow, W&B)
- Creates training orchestration DAGs (Kubeflow, Airflow)
- Builds feature store schemas (Feast)
- Automates retraining and validation workflows
使用场景
- Building end-to-end ML pipelines
- Orchestrating complex training workflows
- Automating the entire model lifecycle
- Implementing feature stores for consistency
- Configuring and managing MLOps tooling
非目标
- Performing quick model prototyping
- Handling one-off data processing tasks
- Replacing interactive notebook exploration
- Addressing non-ML projects
实践
- Data validation
- Reproducibility
- Experiment tracking
- Pipeline orchestration
- Model deployment
- Feature engineering
安装
请先添加 Marketplace
/plugin marketplace add jeffallan/claude-skills/plugin install claude-skills@fullstack-dev-skills质量评分
已验证类似扩展
Orchestrate Ml Pipeline
99Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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.
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Senior Data Engineer
95Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
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.
PyTorch Lightning
100Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.