The MLOps v2 architectural pattern is made up of four main modular elements representing phases of the MLOps lifecycle.
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Data Estate
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Administration & Setup
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Model Development (Inner Loop)
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Model Deployment (Outer Loop)
These elements, the relationships between them, and the personas typically associated with these elements are common for all MLOps v2 scenario architectures though there may some variations in the details of each depending on the scenario.
The base architecture for MLOps v2 for Azure Machine Learning is the Classical Machine Learning scenario on tabular data. Other scenarios like Computer Vision (CV) and Natural Language Processing (NLP) build on or modify this base architecture as appropriate.