The machine learning meta-model with synthetic data (useful for MLOps/feature store), is independent of machine learning solutions (definition in json, data in csv/parquet).

The 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, integration, regression, function, acceptance, performance, shadow, ... tests
• external test coverage (in case, that internal test coverage is not available or weak)
• etc.

Note: You can see real usage of this meta-model in e.g. project qgate-sln-mlrun for testing MLRun/Iguazio solution.

The solution contains this simple structure:

• 00-high-level
  • The high-level view to the meta-model for better understanding
• 01-model
  • The definition contains 01-projects, 02-feature sets, 03-feature vectors, 04-pipelines, 05-ml models, etc.
• 02-data
  • The synthetic data for meta-model in CSV/GZ and parquet formats for party, contact, relation, account, transaction, event, communication, etc.
  • You can also generate your own dataset with requested size (see samples ./02-data/03-size-10k.sh, ./02-data/04-size-50k.sh, etc. and description python main.py generate --help)
• 03-test
  • The information for test simplification e.g. feature vector vs on/off-line data, test/data hints, etc.

Addition details, see structure and see rules

The supported sources/targets for realization (✅ done, ✔ in-progress, ❌ planned), see the definition /spec/targets/ in projects (see specification in JSON files):

• ✅ Redis, ✅ MySQL, ✅ Postgres, ✅ Kafka
• ✅ Pandas, ✅ Parquet, ✅ CSV

The object relations for key objects in meta-model, plus splitting these objects in packages (01-model/01-project, 01-model/02-feature-set, 02-data, etc.).

The basic feature sets and relations between them.

The derived feature sets and relations between them.