Machine Learning Pipeline Automation

Data Wrangling

Cortex ingests raw behavioral and attribute data at massive scales and provides tools to automatically aggregate and clean the data.

Feature Engineering

Models learn from features, not raw behavioral data. Cortex's automated feature engineering searches across multiple time windows and nonlinear transformations.

Model Selection

Cortex eliminates guesswork by automatically testing combinations of algorithms and model parameters to build the best pipelines.

Help with Data Wrangling at Scale

Data wrangling accounts for 90-95% of the effort in building a Machine Learning Pipeline. Cortex provides your team tools to help automate data wrangling. These tools can be leveraged at massive data scales such as hundreds of millions of unique users and billions of behavioral events.

Automate Feature Engineering

We know that feature engineering can be challenging, especially at massive data scales. Cortex provides robust automated feature engineering tools which work across multiple time windows, various nonlinear transformations, and incorporate metadata. We’ve seen which features work best across some of the largest global businesses, now enable your team to take advantage of these learnings!

Find the Best Model for Every Business Problem

Choosing the best model often requires trial and error. Your team can take advantage of automated Model Selection and Hyper-Parameter Optimization techniques to make integration and value creation faster.

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