Machine Learning Operations (MLOps)
Machine Learning Operations
Machine Learning Operations (MLOps or ML Ops) are practices that aim to deploy and maintain machine learning models in production reliably and efficiently.
- The word is a compound of "machine learning", and the development practices of DevOps in the software field are continuous.
- Machine learning models are tested and developed in isolated experimental systems.
- When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.
- Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements.
- MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps.
- MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.