TranzAI feature store

Generating a new feature set requires a significant amount of work and can, most of the time, only be achieved through trial and error.

From defining features to defining their extraction graph, the TranzAI platform provides a friendly GUI to manage, collaboratively, the implementation of your feature store and the deployment of the associated Feature Extraction pipelines.

Features and ontology

Several studies (MIT) have shown that the interpretability of features plays a major role in the adoption of AI by business leaders and the confidence they can place in AI research.

Very often, a lot of confusion stems from the features, not the model itself. This confusion can not only affect trust and interpretability, but can also induce errors at the MLOps level when ML engineers have to choose the most efficient feature extraction path and storage method to extract and update features once the models are in production.

Through its tight integration with TranzAI's Ontology Manager, the feature store benefits from the consolidation of business knowledge achieved at the ontology level.

Features and data lineage

Transforming raw data into features that can be used for machine-learning model design and training requires several steps that must be properly documented to implement reliable and reproducible research processes and facilitate the transfer of models to production.

The metadata-driven nature of the TranzAI platform provides automatic process documentation accessible to all stakeholders within a data science project.