MLOps (Machine Learning Operations)

Overview

MLOps brings DevOps principles to machine learning workflows, enabling reliable, scalable, and reproducible ML systems in production. It covers the entire ML lifecycle from experimentation to deployment and monitoring.

Core Principles:

• Reproducibility: Version everything (code, data, models, environments)
• Automation: Automate training, testing, deployment pipelines
• • Monitoring: Track model performance, data drift, system health
• Collaboration: Bridge data scientists, engineers, and operations
• Governance: Ensure model compliance, explainability, and auditing

Level 1: Quick Reference