mlops-engineer

name	mlops-engineer
type	reference
description	Provides MLOps patterns for ML CI/CD pipelines, model registries, monitoring, and data drift detection. Use when setting up ML infrastructure or when the user mentions MLOps, model deployment, ML pipeline, or model monitoring.
paths	["**/*.py","**/Dockerfile","**/requirements*.txt","**/mlflow*","**/*.yaml"]
effort	4
allowed-tools	Read, Glob, Grep, Write, Edit, Bash
user-invocable	true
when_to_use	When building ML pipelines, experiment tracking systems, or model registries with MLflow or Kubeflow

MLOps Engineer

Tool selection matrix

Need	Tool	When to use
Experiment tracking	MLflow	Open-source, self-hosted
Experiment tracking	W&B	Cloud, rich visualization
Pipeline orchestration	Kubeflow	Kubernetes-native
Pipeline orchestration	Prefect	Python-first, dynamic
Data version control	DVC	Git-based datasets & models
Feature store	Feast	Open-source, online+offline
Model serving	KServe	K8s serverless inference
Model serving	SageMaker Endpoints	AWS managed
Monitoring / drift	Evidently	Open-source, alerting
CI/CD for ML	GitHub Actions + DVC	Lightweight

MLflow: experiment tracking + model registry

import mlflow
import mlflow.sklearn

mlflow.set_tracking_uri("http://mlflow-server:5000")
mlflow.set_experiment("model-training")

with mlflow.start_run():
    # Log params
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("max_depth", 5)

    # Train
    model = train(X_train, y_train)
    metrics = evaluate(model, X_test, y_test)

    # Log metrics
    mlflow.log_metric("accuracy", metrics["accuracy"])
    mlflow.log_metric("f1", metrics["f1"])

    # Log model + register
    mlflow.sklearn.log_model(
        model, "model",
        registered_model_name="fraud-detector",
    )

# Promote to production via API
client = mlflow.tracking.MlflowClient()
client.transition_model_version_stage(
    name=, version=, stage=
)