dotData FeatureFactory on Python

“Its exceptional data management and feature engineering capabilities make it especially suitable for the most challenging use cases…feature engineering is powerful and scalable, even across tens of tables with billions of rows.”

Key Features

FEATURE DISCOVERY
AND ENGINEERING
FEATURE QUALITY
& SELECTION
FEATURE TRANSPARENCY
& INSIGHTS
FEATURE PIPELINE
& QUERY
Feature discovery from raw data

Features from Relational Data

Process transactional, temporal geo-locational, or text data directly from multi-table structured data. Explore millions of feature patterns by auto-joining multiple tables automatically.

Automatic data cleansing and prep for ML

Find Features that Matter

evaluate features with patented supervised learning techniques to avoid temporal leakage, collinearity, overfitting, and more. Auto-cleanse data with value canonicalization, outlier detection, and missing value imputation.

Discover ML transparency

Quantitative & Qualitative Transparency

Understand features through metrics. From basic statistical measures to advanced feature importance scores. Generate feature explanations, blueprints, and queries for full transparency of feature pipeline and lineage.

Build scalable feature pipelines

Production-ready Feature Pipelines

Generate ready-to-deploy feature pipelines and minimize the headache between data science and data engineering teams when productionalizing feature queries.

Justin Shoolery - dotData Client

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