MLOps is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably and efficiently. It bridges the gap between model development and production deployment.
graph LR
A[Data Science] -->|Without MLOps| B[Manual Deployment]
B --> C[Inconsistent Results]
C --> D[Production Failures]
A -->|With MLOps| E[Automated Pipeline]
E --> F[Reproducible Models]
F --> G[Reliable Production]
The gap: ~85% of ML projects never make it to production. MLOps addresses this by providing the engineering practices needed to deploy, monitor, and maintain ML systems reliably.
graph TD
A[1. Problem Definition] --> B[2. Data Collection & Labeling]
B --> C[3. Data Validation & Exploration]
C --> D[4. Feature Engineering]
D --> E[5. Model Training & Tuning]
E --> F[6. Model Evaluation]
F --> G{Meets Criteria?}
G -->|No| D
G -->|Yes| H[7. Model Registration]
H --> I[8. Model Deployment]
I --> J[9. Monitoring & Alerting]
J --> K{Drift Detected?}
K -->|Yes| B
K -->|No| J
graph LR
A[Code Change] --> B[CI: Build & Test]
B --> C[CT: Retrain Model]
C --> D[Evaluate Model]
D --> E{Quality Gate?}
E -->|Pass| F[CD: Deploy Model]
E -->|Fail| G[Alert & Investigate]
F --> H[Monitor in Production]
What is MLOps and why is it important?
MLOps applies DevOps principles to ML systems. It’s important because ML systems have unique challenges: data dependencies, model drift, non-deterministic training, and complex reproducibility requirements. Without MLOps, most ML projects fail in production.
How does MLOps differ from DevOps?
MLOps manages three artifacts (code, data, model) instead of just code. It requires data versioning, experiment tracking, model evaluation gates, drift monitoring, and retraining pipelines on top of standard CI/CD.
What is training-serving skew and how do you prevent it?
When the feature computation logic differs between training and serving, causing models to see different data in production. Prevention: (1) Share feature computation code. (2) Use a feature store. (3) Validate features in both pipelines.
How do you version ML models?
Track model artifacts (weights, config), training code version (git commit), data version (DVC hash), hyperparameters, and evaluation metrics. Store in a model registry with metadata linking all these together.
Design an ML system that retrains automatically.
Scheduled or drift-triggered retraining → automated training pipeline → evaluation against production model → quality gate (must beat baseline) → canary deployment → monitoring → rollback if degraded.
MLOps is essential for moving ML from research to production. It ensures reproducibility, scalability, and reliability of ML systems through automation, monitoring, and best practices borrowed from DevOps and Data Engineering.