qgate-model
ML/AI meta-model, used in MLRun/Iguazio/Nuclio, see qgate-sln-
Stars: 322
QGate-Model is a machine learning meta-model with synthetic data, designed for MLOps and feature store. It is independent of machine learning solutions, with definitions in JSON and data in CSV/parquet formats. This meta-model is useful for comparing capabilities and functions of machine learning solutions, independently testing new versions of machine learning solutions, and conducting various types of tests (unit, sanity, smoke, system, regression, function, acceptance, performance, shadow, etc.). It can also be used for external test coverage when internal test coverage is not available or weak.
README:
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 descriptionpython 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.
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