Changing business opportunities and needs are driving a more data-driven approach to growth. The increased focus on data is placing increased pressure on Data Science teams who have to deliver more AI/ML models. The quality of models is directly impacted by the quality of input data (i.e. “feature tables”) and by the breadth of feature hypotheses.
dotDataPy’s Automated feature engineering augments your ability to explore higher-order hypotheses, helps you expand your feature space, and extracts the maximum potential from complex data.
- Automatically explore your data to discover and build AI-features
- Broad data-type compatibility unlocks the maximum power of your data
- Generate feature transformation queries that are ready to deploy to production
- Built to fit in your Python ML workflow
Augment your feature space with AI-features
Great ML models need great features. dotData Py uses an AI algorithm to automatically hypothesize, explore, build, and validate features and AI-features augments your feature space to develop greater models.
Unlock the maximum power from your complex data
More data promises better performance. dotData Py explores millions of features from relational, transactional, temporal, geo-locational, and text data and discovers deeper features, automatically. Try more data with automated feature engineering and find what works and what not, faster.
Deploy feature transformers in production faster
Production models need production features. dotData Py generates feature transformation queries with production quality and scalability. Deploy features for your ML models and deliver ML applications faster.
Work on your Python/ML workflow seamlessly
Trial-and-error is the only way to build great features and models. With its dataframe interface, dotData Py fits in your favorite Python-based data science environment and becomes a seamless part of your iterative AI/ML development workflow.