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Overview of Pipeline Types

Machine Learning Pipelines are a way to automatically transform raw data into machine learning predictions. Cortex is a platform that allows anyone within an organization to easily create predictions based on data that has already been captured. Below, we will go over the different pipeline types available within Cortex, and list some examples to give a better understanding of what is possible.

Which Pipeline Should I Use?

Cortex offers multiple pipeline and prediction types. The following diagram will help explain which pipeline type is best suited for different predictions.

Future Events Pipelines

Describe an event in everyday terms, and Cortex will predict the likelihood that each user completes that event in the future. If you can describe it with your data, you can predict it in Cortex – any type of event, any set of conditions, and any future window of time. Learn how to build your own Future Events Pipeline.

Future Event Pipeline Examples

Uplift Pipelines

Specify a conversion event, and provide details about an intervention designed to drive those conversions. Cortex will predict the impact of your intervention on each user’s likelihood of converting in the future. Learn how to build your own Uplift Pipeline.

Uplift Pipeline Examples

Recommendation Pipelines

Select a set of items that you’d like to recommend. Cortex will learn the unique preferences of your users and generate personalized recommendations for each one. You may also specify a particular type of event for these recommendations to optimize. Learn how to build your own Recommendations Pipeline.

Recommendations Pipeline Examples

Regression Pipelines

Upload a list of users along with values of some numeric attribute. Cortex will predict the value of that attribute for each of your remaining users. Learn how to build your own Regression Pipeline.

Regression Pipeline Examples

Look Alike Pipelines

Upload a list of users which share a common characteristic. Cortex will search through the rest of your users and score each one in terms of how similarly it looks and acts to the uploaded set. If you have a list of users without the characteristic, use a Classification pipeline instead. Learn how to build your own Look Alike Pipeline.

Look Alike Pipeline Examples

Classification Pipelines

Upload a list of users which share a common characteristic, and another list of users that don’t exhibit the characteristic. Cortex will score each remaining user in terms of its likelihood to belong to the first group rather than the second.

Like Look Alike pipelines, Classification is used to determine whether your users are likely to belong to a certain group. If you have access to negative labels, you should always use a Classification pipeline to solve this type of problem, since negative labels give Cortex more information from which to learn. Learn how to build your own Classification Pipeline.

Classification Pipeline Examples

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