MLOps Engineering

概要

Bridge the gap between data science and production engineering with battle-tested standards for Machine Learning Operations. This skill provides comprehensive guidance on building reproducible ML workflows, managing model registries, and deploying models across batch, real-time, and streaming environments. Whether you are setting up experiment tracking with MLflow, orchestrating pipelines with Kubeflow, or implementing automated drift detection to ensure model reliability, this resource offers the implementation patterns and checklists needed to maintain healthy, production-grade ML systems.

主な機能

• Comprehensive model versioning and experiment tracking guidelines with MLflow
• 8 GitHub stars
• Automated data and concept drift detection strategies for production monitoring
• Standardized deployment strategies for batch, real-time, and streaming inference
• End-to-end ML pipeline design and orchestration patterns using Kubeflow and Airflow
• Reproducible feature engineering workflows using DVC and feature stores

ユースケース

• Scaling ML models from local Jupyter notebooks to production-ready Kubernetes deployments
• Implementing automated retraining triggers based on statistical drift detection in production
• Establishing a centralized model registry and experiment tracking system for data science teams