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MLOps
Why MLOps
Operationalizing ML/AI models with focus on automation, collaboration, and reliability.
Building is easy, sustaining is hard.
Remember dieting/excercise?
- Companies moved past “build model in Jupyter” → now productionize models.
- 80% of ML projects fail due to lack of deployment + monitoring strategy.
- MLOps bridges Data → Model → Production.
Industry requirement
- Versioning models
- Monitoring drift
- Scalable deployment
- Regulatory compliance (audit trail, lineage)
Lifecycle
- Data ingestion -> data validation & quality checks -> feature engineering
- Model training -> validation -> experiment tracking & versioning
- Deployment (batch, real-time, API) -> rollback capabilities
- Monitoring
- Data drift (input distribution)
- Model drift (prediction accuracy)
- Concept drift (feature:label relationship)
- Infrastructure performance
- Continuous improvement -> retraining & iteration
Cross-Functional Teams
- Data Engineers
- Data Scientists
- ML Engineers
- Platform/DevOps Engineers
- Product Managers
Key Capabilities
- Reproducibility
- Scalability
- Governance & compliance
- Automated CI/CD pipelines
