MLOps (Machine Learning Operations)

• MLOps ensures that AI models are not only built but also deployed, monitored, and improved in a reliable and repeatable manner.
• Key Concepts
  • Extension of DevOps principles to machine learning workflows
  • Enables continuous delivery and lifecycle management of models
• Core MLOps Principles
  • Version Control: manage versions of data, code, and models with rollback capability
  • Automation: of all stages, including data ingestion, pre-processing, training, etc…
  • Continuous Integration: test models consistently
  • Continuous Delivery: deploy models safely to production
  • Continuous Retraining: adapt models to new data and changing conditions
  • Continuous Monitoring: track model performance, identify issues, and trigger retraining