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.
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:
Use third-party data to understand your customers – and your competitors' customers – more deeply.
Identify and reach your most valuable customers and prospects with campaign-specific audiences.