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Terminal · npx$
npx skills add https://github.com/jeffallan/claude-skills --skill ml-pipelineWorks with Paperclip
How Ml Pipeline fits into a Paperclip company.
Ml Pipeline drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.
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Pre-configured AI company — 18 agents, 18 skills, one-time purchase.
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SKILL.md159 linesExpandCollapse
---name: ml-pipelinedescription: "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."license: MITmetadata: author: https://github.com/Jeffallan version: "1.1.0" domain: data-ml triggers: ML pipeline, MLflow, Kubeflow, feature engineering, model training, experiment tracking, feature store, hyperparameter tuning, pipeline orchestration, model registry, training workflow, MLOps, model deployment, data pipeline, model versioning role: expert scope: implementation output-format: code related-skills: devops-engineer, kubernetes-specialist, cloud-architect, python-pro--- # ML Pipeline Expert Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows. ## Core Workflow 1. **Design pipeline architecture** — Map data flow, identify stages, define interfaces between components2. **Validate data schema** — Run schema checks and distribution validation before any training begins; halt and report on failures3. **Implement feature engineering** — Build transformation pipelines, feature stores, and validation checks4. **Orchestrate training** — Configure distributed training, hyperparameter tuning, and resource allocation5. **Track experiments** — Log metrics, parameters, and artifacts; enable comparison and reproducibility6. **Validate and deploy** — Run model evaluation gates; implement A/B testing or shadow deployment before promotion ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When ||-------|-----------|-----------|| Feature Engineering | `references/feature-engineering.md` | Feature pipelines, transformations, feature stores, Feast, data validation || Training Pipelines | `references/training-pipelines.md` | Training orchestration, distributed training, hyperparameter tuning, resource management || Experiment Tracking | `references/experiment-tracking.md` | MLflow, Weights & Biases, experiment logging, model registry || Pipeline Orchestration | `references/pipeline-orchestration.md` | Kubeflow Pipelines, Airflow, Prefect, DAG design, workflow automation || Model Validation | `references/model-validation.md` | Evaluation strategies, validation workflows, A/B testing, shadow deployment | ## Code Templates ### MLflow Experiment Logging (minimal reproducible example) ```pythonimport mlflowimport mlflow.sklearnfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score, f1_scoreimport numpy as np # Pin random state for reproducibilitySEED = 42np.random.seed(SEED) mlflow.set_experiment("my-classifier-experiment") with mlflow.start_run(): # Log all hyperparameters — never hardcode silently params = {"n_estimators": 100, "max_depth": 5, "random_state": SEED} mlflow.log_params(params) model = RandomForestClassifier(**params) model.fit(X_train, y_train) preds = model.predict(X_test) # Log metrics mlflow.log_metric("accuracy", accuracy_score(y_test, preds)) mlflow.log_metric("f1", f1_score(y_test, preds, average="weighted")) # Log and register the model artifact mlflow.sklearn.log_model(model, artifact_path="model", registered_model_name="my-classifier")``` ### Kubeflow Pipeline Component (single-step template) ```pythonfrom kfp.v2 import dslfrom kfp.v2.dsl import component, Input, Output, Dataset, Model, Metrics @component(base_image="python:3.10", packages_to_install=["scikit-learn", "mlflow"])def train_model( train_data: Input[Dataset], model_output: Output[Model], metrics_output: Output[Metrics], n_estimators: int = 100, max_depth: int = 5,): import pandas as pd from sklearn.ensemble import RandomForestClassifier import pickle, json df = pd.read_csv(train_data.path) X, y = df.drop("label", axis=1), df["label"] model = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42) model.fit(X, y) with open(model_output.path, "wb") as f: pickle.dump(model, f) metrics_output.log_metric("train_samples", len(df)) @dsl.pipeline(name="training-pipeline")def training_pipeline(data_path: str, n_estimators: int = 100): train_step = train_model(n_estimators=n_estimators) # Chain additional steps (validate, register, deploy) here``` ### Data Validation Checkpoint (Great Expectations style) ```pythonimport great_expectations as ge def validate_training_data(df): """Run schema and distribution checks. Raise on failure — never skip.""" gdf = ge.from_pandas(df) results = gdf.expect_column_values_to_not_be_null("label") results &= gdf.expect_column_values_to_be_between("feature_1", 0, 1) if not results["success"]: raise ValueError(f"Data validation failed: {results['result']}") return df # safe to proceed to training``` ## Constraints **Always:**- Version all data, code, and models explicitly (DVC, Git tags, model registry)- Pin dependencies and random seeds for reproducible training environments- Log all hyperparameters, metrics, and artifacts to experiment tracking- Validate data schema and distribution before training begins- Use containerized environments; store credentials in secrets managers, never in code- Implement error handling, retry logic, and pipeline alerting- Separate training and inference code clearly **Never:**- Run training without experiment tracking or without logging hyperparameters- Deploy a model without recorded validation metrics- Use non-reproducible random states or skip data validation- Ignore pipeline failures silently or mix credentials into pipeline code ## Output Format When implementing a pipeline, provide:1. Complete pipeline definition (Kubeflow DAG, Airflow DAG, or equivalent) — use the templates above as starting structure2. Feature engineering code with inline data validation calls3. Training script with MLflow (or equivalent) experiment logging4. Model evaluation code with explicit pass/fail thresholds5. Deployment configuration and rollback strategy6. Brief explanation of architecture decisions and reproducibility measures ## Knowledge Reference MLflow, Kubeflow Pipelines, Apache Airflow, Prefect, Feast, Weights & Biases, Neptune, DVC, Great Expectations, Ray, Horovod, Kubernetes, Docker, S3/GCS/Azure Blob, model registry patterns, feature store architecture, distributed training, hyperparameter optimization