ml-pipeline
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill ml-pipeline- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ml-pipeline automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
ML Pipeline Expert
Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows.
Core Workflow
- Design pipeline architecture — Map data flow, identify stages, define interfaces between components
- Validate data schema — Run schema checks and distribution validation before any training begins; halt and report on failures
- Implement feature engineering — Build transformation pipelines, feature stores, and validation checks
- Orchestrate training — Configure distributed training, hyperparameter tuning, and resource allocation
- Track experiments — Log metrics, parameters, and artifacts; enable comparison and reproducibility
- 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)
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
import numpy as np
# Pin random state for reproducibility
SEED = 42
np.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)
from kfp.v2 import dsl
from 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)
import 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:
- Complete pipeline definition (Kubeflow DAG, Airflow DAG, or equivalent) — use the templates above as starting structure
- Feature engineering code with inline data validation calls
- Training script with MLflow (or equivalent) experiment logging
- Model evaluation code with explicit pass/fail thresholds
- Deployment configuration and rollback strategy
- 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
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the ml-pipeline skill do?
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.
How do I install ml-pipeline?
Run npx skills add https://github.com/jeffallan/claude-skills --skill ml-pipeline in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ml-pipeline/ (personal) or .claude/skills/ml-pipeline/ (project).
Which AI agents can use ml-pipeline?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ml-pipeline safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains ml-pipeline?
It is published by jeffallan in jeffallan/claude-skills. It has 3,896 recorded installs.
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