Artificial Intelligence
What is MLOps
DevOps practices for machine learning
MLOps (Machine Learning Operations) — a set of practices combining ML model development (ML) and operational deployment (Ops) to automate and standardize the machine learning lifecycle.
Key Components
- Version Control — versioning data, models, and code
- CI/CD for ML — automated training and deployment pipelines
- Feature Store — centralized feature storage
- Model Registry — registry of trained models
- Monitoring — tracking model quality in production
MLOps Pipeline Stages
- Data Pipeline — data collection, cleaning, transformation
- Training Pipeline — model training and validation
- Deployment Pipeline — production deployment
- Monitoring Pipeline — monitoring and alerting
MLOps Tools
- MLflow — experiment and model management
- Kubeflow — ML platform on Kubernetes
- DVC — data versioning
- Weights & Biases — experiment tracking
- Seldon / BentoML — model serving
Business Benefits
- Acceleration — faster from idea to production
- Quality — drift and degradation control
- Scalability — process standardization
- Collaboration — unified environment for DS and engineers