Automate Every Step of the Machine Learning Pipeline

Building Machine Learning Pipelines is more than just picking the right model, especially at the large data volumes of most companies. With Cortex, we automate every step in building a Machine Learning Pipeline, starting with Data Wrangling and Feature Engineering which is then used to find the most accurate Model for each Prediction.

Data Wrangling

Cortex ingests raw data from multiple sources at massive scale and automatically aggregates and cleans the data. This allows numerous predictions to be made from the same data set.

Feature Engineering

Machine Learning models learn off of features, and not raw data. Cortex automatically creates the necessary features from your raw data, saving time and guess work in coming up with the right data for your predictions.

Model Selection

Cortex takes the guesswork out of Machine Learning by automatically testing the right Features, Algorithms, and Model Parameters to make the most accurate predictions.

Prediction Generation

The final step of automation is generation predictions on a per-user basis. These predictions can be retrieved through an API or exported in batch on a recurring schedule.

Say Goodbye to Data Wrangling

Data wrangling accounts for 90-95% of the effort in building a Machine Learning Pipeline, and progress is often slowed by the unforeseen challenges it presents. Cortex automates this process end-to-end and at massive data scales.  With every step of the Machine Learning Pipeline automated for you in Cortex, your team is free to focus on strategy and how to optimize results with Machine Learning.

Automatic Feature Creation and Selection

Creating features from raw data at large scales is a challenging task for any team.  Having to do this process numerous times for multiple predictions often makes Machine Learning inaccessible to most teams.  Cortex can automatically transform existing data into Features, opening up Machine Learning to more use cases and teams.

The Right Model for Every Prediction

Choosing the best model for a prediction often requires guesswork and trial and error. This is not only time consuming but also requires deep expertise in AI and machine learning. By automating Model Selection, Cortex gives the power of machine learning to everyone in an organization.

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