The machine learning meta-model (useful for MLOps/feature store), is independent of machine learning solutions (definition in json, data in csv/parquet). It can be used with various of ML/MLOps solutions with or without FeatureStore.
This meta-model is suitable for:
- compare capabilities and functions of machine learning solutions (as part of RFP/X and SWOT analysis)
- independent test new versions of machine learning solutions (with aim to keep quality in time)
- unit, sanity, smoke, system, reqression, function, acceptance, performance, shadow, ... tests
- external test coverage (in case, that internal test coverage is not available or weak)
- etc.
The solution contains this simple structure:
- 00-high-level
- The high-level view to the meta-model for better understanding
- Note: The HL meta-model is drawn in Enterprise Architect (from Sparx)
- 01-model
- The definition contains 01-projects, 02-feature sets, 03-feature vectors, etc. in JSON format
- This model is designed for these use cases
- 02-data
- The data for meta-model in CSV/GZ format (future support parquet) for party, account, transaction, etc.
Addition detail, see
