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