Anovos, a scalable, open source feature engineering solution
Build stable, resilient machine learning models.
One of the biggest impediments to the large scale use of machine learning (ML) is the unpredictable performance of operationalized models - models test out well but underperform when deployed. As ML has evolved, this need to build resilient models has become a key driver of its widespread adoption in organizations.
To proactively address this, Mobilewalla has released an open source project, Anovos, to help data scientists and engineers understand the stability of your data prior to building and implementing your models. Anovos offers a comprehensive feature engineering toolkit for data scientists.
Anovos enables data scientists to build sustainable ML predictive models, essential to support core operations such as acquisition, retention, risk analysis, and logistics.
Effective Feature Engineering
Feature engineering is a manual, resource-intensive process that requires ample data, careful analysis, expert judgment, and some degree of luck. This complexity creates ample room for error.
Mobilewalla helps data scientists streamline the feature discovery process and improve predictive modeling with two solutions:
Predictive Features – The Mobilewalla Feature Mart helps data scientists improve modeling accuracy, efficiency and results with access to a library of pre-defined highly predictive features that are not natively available in an organization’s first party data
Feature Engineering Tools – Anovos, Mobilewalla's open source project that supports the feature engineering process including data ingestion and cleansing data stability/drift analysis, EDA, feature recommendation and transformation
With Anovos you can:
Perform the following data functions including ingestion from any source, cleansing, analytics, feature generation and transformation.
Seamlessly analyze data providing a comprehensive set of curated analytics and evaluating the health of the data and the potential impact on modeling outcomes.
Build drift resistant, stable features through recommendation from a library of existing features or transformation using pre-built functions .
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Use third-party data to understand your customers – and your competitors' customers – more deeply.