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Feature Engineering

By sharing features inside a common feature store and designing a global set of feature extraction pipelines, AI teams can rely on standardized processes that can be shared across teams and models.

Models deployment and maintenance is also much easier as all these features and pipelines are directly available through Comfiz dataops platform and built on top of a ML-optimized logical data model.

Comfiz Feature Store

For most AI use cases aiming at improving current business processes, the best results are achieved through the optimization and combination of 3 processes:
- data sources selection and consolidation
- features extraction and selection
- algorithms selection and models training

Feature engineering is inherent to machine learning. Features help describe the structures inherent to a set of data. As a representation of the underlying problem addressed in a use case, smart features selection gives data scientists the capacity to improve model accuracy, to use less complex models and also to explain how models are built and can be maintained.


Benefits of good feature engineering

The quality of features extracted from raw data directly influences the performances of predictive models. Features are definitely not the only factor that will affect performances, but experience shows that, good feature engineering offers a number of benefits.

The possibility to use less complex models is certainly a critical one as simpler models are:
- faster to run
- easier to understand
- easier to maintain