MLOps
Definition
MLOps (Machine Learning Operations) is the practice of applying DevOps principles to ML systems, automating and standardizing the entire ML lifecycle from data preparation through model deployment, monitoring, and retraining.
Why It Matters
Most ML projects never reach production. The gap between “working notebook” and “reliable production system” is enormous. MLOps bridges this gap by treating ML systems with the same rigor as traditional software.
Without MLOps, teams face reproducibility nightmares (“it worked on my machine”), deployment chaos (manual processes, no rollback), silent failures (model degradation goes unnoticed), and technical debt (spaghetti pipelines nobody understands).
For AI engineers, MLOps skills differentiate implementers from researchers. Companies don’t need another person who can train models. They need people who can ship and maintain them. MLOps is where AI engineering becomes real engineering.
Implementation Basics
MLOps maturity progresses through levels:
Level 0: Manual Data scientists work in notebooks. Models are manually deployed (if at all). No automation, no monitoring, no reproducibility. Most organizations start here.
Level 1: ML Pipeline Automation Automated training pipelines. Feature engineering, training, and validation run automatically when triggered. Models are versioned. Basic monitoring exists. This is the minimum viable MLOps.
Level 2: CI/CD for ML Automated testing of data, features, and models. Continuous training on new data. Automated deployment with canary releases. A/B testing for model evaluation. Full observability and alerting.
Key MLOps Components:
- Version Control: Code, data, models, configs all versioned (Git, DVC)
- Pipeline Orchestration: Automated workflows (Airflow, Kubeflow, Prefect)
- Experiment Tracking: Log and compare training runs (MLflow, W&B)
- Model Registry: Store and manage model versions (MLflow, cloud registries)
- Serving Infrastructure: Deploy models as services (Docker, Kubernetes, managed endpoints)
- Monitoring: Track performance, drift, and system health (custom + specialized tools)
Start with the highest-impact component for your situation. If you’re manually deploying, automate deployment first. If models degrade silently, add monitoring first. MLOps is about incremental improvement, not Big Bang transformation.
The goal isn’t MLOps for MLOps’ sake. It’s reliable, maintainable ML systems that deliver business value.
Source
MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops).
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning