ml-pipeline

name	ml-pipeline
description	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	MIT
metadata	{"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 components
2. Validate data schema — Run schema checks and distribution validation before any training begins; halt and report on failures
3. Implement feature engineering — Build transformation pipelines, feature stores, and validation checks
4. Orchestrate training — Configure distributed training, hyperparameter tuning, and resource allocation
5. Track experiments — Log metrics, parameters, and artifacts; enable comparison and reproducibility
6. 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:

1. Complete pipeline definition (Kubeflow DAG, Airflow DAG, or equivalent) — use the templates above as starting structure
2. Feature engineering code with inline data validation calls
3. Training script with MLflow (or equivalent) experiment logging
4. Model evaluation code with explicit pass/fail thresholds
5. Deployment configuration and rollback strategy
6. 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