Definition #
MLOps is the set of engineering practices that automate and standardize the development, deployment, and monitoring of machine learning systems in production.
Key Characteristics #
- CI/CD for models (continuous training)
- Model versioning (DVC, MLflow)
- Monitoring drift/performance decay
- Feature store integration
Why It Matters #
Teams using MLOps deploy models 7x faster with 60% fewer errors (McKinsey, 2023).
Common Use Cases #
- Retail demand forecasting pipelines
- Fraud detection system updates
- A/B testing model variants
Examples #
- Tools: Kubeflow, MLflow, Weights & Biases
- Cloud services: AWS SageMaker Pipelines, GCP Vertex AI
- Frameworks: TFX, PyTorch Serve
FAQs #
Q: How is MLOps different from DevOps?
A: Adds data/experiment tracking and model-specific monitoring (e.g., concept drift).
Q: Can small teams use MLOps?
A: Yes—start with lightweight tools like Prefect for orchestration.