Building Machine Learning Pipelines is more than picking the right model. Cortex's robust toolset helps automate every step of the Machine Learning Pipeline, starting with data wrangling and feature engineering all the way through model selection.
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.
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!
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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