The first feature engine with time travel

True time travel is calculating feature values at arbitrary, data-dependent, points in time—without leakage. Experience how Kaskada brings time travel to feature engineering, providing the power to instantly recompute feature values on demand.

Time travel with Kaskada

With Kaskada, you can choose to calculate all feature values at any point in time. Or you can calculate the feature values for each entity at the time an event occurred. For instance, calculate all features at the exact time a user made a large purchase, when a customer churned out 30 days after their planned subscription date, or at the time of a fraudulent transaction.

Use these point-in-time and event-driven feature values to train models without risk of leakage. When you're ready, you can compute the same feature values with a time of "now" to make new predictions using a live model in production.

Time travel for feature engineering without leakage requires:

Historical feature value generation

Compute directly from event-based data to try new features

Quickly try ideas on historical data by computing the prediction and label times for each training example directly from event times and fields. True time travel allows for each training example time to be different relative to each other and based on predicates.Learn more

Ordered processing to prevent leakage

Flexible time selection requires support for temporal joins

Feature values need to be calculated and joined in order. Joining with the contents of a relational database reflects the current values, not the values at the relevant times in the past. True time travel enables joining values between different entities, at precise times—without leakage.Learn more in this case study

Eliminating data discrepancies in production

Shared feature definitions to power live models

Training a model on examples computed directly from event based data requires the same data shape available in production. Writing different code for production risks model degradation. True time travel for machine learning allows for feature definitions to be shared.Learn how in this quickstart guide

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