Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers and AI enthusiasts. Following this trend, the Continuous Delivery Foundation SIG MLOps differentiates the ML models management from traditional software engineering and suggests the following MLOps capabilities:
- MLOps aims to unify the release cycle for machine learning and software application release.
- MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
- MLOps enables the application of agile principles to machine learning projects.
- MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
- MLOps reduces technical debt across machine learning models.
- MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.