mlops_skill

Overview

This skill documents practical MLOps engineering for building reliable, reproducible, and scalable production ML systems. It covers pipeline design, experiment tracking, model versioning, deployment strategies, drift detection, and monitoring using tools like MLflow, Kubeflow, DVC, and model registries. The goal is to enable teams to move models from experimentation to continuous production safely and efficiently.

How this skill works

I provide patterns, checklists, and code snippets that inspect lifecycle stages: data versioning and validation, reproducible training, model registry operations, serving strategies, and production monitoring. The content maps tools to tasks (MLflow for tracking and registry, Kubeflow/Airflow for orchestration, Feast for features, Prometheus/Grafana for monitoring) and shows how to wire automated retraining, canary rollouts, and drift alerts into CI/CD. Practical commands and short examples illustrate common workflows and integrations.

When to use it

• Building a repeatable experiment-to-deployment pipeline
• Setting up model versioning and a model registry
• Implementing monitoring, data drift, and automated retraining
• Defining CI/CD for models and production deployment strategies
• Integrating feature stores and production feature retrieval

Best practices

• Version everything: code, data, models, and environments with Git, DVC, and registries
• Automate validation gates: data checks, model evaluation, and inference tests in pipelines
• Use experiment tracking to capture parameters, metrics, and artifacts consistently (e.g., MLflow)
• Adopt safe deployment patterns: canary, shadow, and A/B testing with rollback procedures
• Monitor both system metrics and model-specific signals: data drift, concept drift, latency, and prediction distributions

Example use cases

• End-to-end pipeline: DVC for data, MLflow tracking, Kubeflow retraining, FastAPI serving, Prometheus monitoring
• Drift detection service: baseline stats, KS/chi-square tests, alerts, and automated retrain triggers
• A/B testing: deploy two model versions, route traffic with Thompson Sampling, select winner and rollout
• Feature store integration: Feast feature views, materialize historical data, online serving for low-latency inference
• Batch and streaming inference: Airflow DAGs for batch jobs and Kafka consumers for streaming predictions