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).

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.

Note: You can see real usage 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-ml models, etc. in JSON format
• 02-data
  • The synthetic data for meta-model in CSV/GZ and parquet formats for party, account, transaction, event, communication, etc.
  • You can also generate your own dataset with requested size (see sample ./02-data/03-size-10k.sh or 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 detail, see

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

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