Build better features.

Make better predictions.

Kaskada increases the impact of machine learning
by computing features in real-time.

Now hiring: Backend and Full-stack engineers. Learn more >

Designing features is an art.
Deploying features is a pain.

Kaskada machine learning studio empowers data scientists to bring features from idea to production in an easy-to-use platform.


Connect to streaming and historical data. Access all events in the same way, regardless of source.

Design & Visualize

Clean data and design features using an interactive, intuitive interface. Visualize data and see correlations.


Deploy features to production ⁠— no engineers required. Iterate and refine in minutes, not weeks.


See changes to your source data and events as they come in and over time. Quickly diagnose issues.

Fresher features = more accurate predictions

Most machine learning systems use data that is days or even weeks old.

Nearly all production machine learning systems use stale data. Machine learning features used in production are typically updated using batch-based pipelines that run anywhere from every few hours to every few weeks. Using stale data makes the models and resulting predictions much less accurate for quickly-changing user behavior or environments.


Kaskada generates machine learning features in real time based on streaming data. With fresh features, your machine learning models can make more accurate, impactful predictions.

Kaskada Machine Learning Studio is ideal for:

Recommendation engines

Ranking & search

Inventory / Supply chain


Predictive pricing

Work smarter, together

Share ideas and iterate on features.

Collaborative Projects

Create and view all features for a specific model in one location.

Versioning and history

View changes to projects over time and see who changed what and when. 

Feature Inspectability

Understand how features are computed and encoded and explore data sources.

Add value to machine learning across your organization

Data Scientists

Own the feature lifecycle from idea to production.


Visualize features and drill into data with a few clicks.

Data Leaders

Reduce churn between data science and engineering.


Increase collaboration and speed of innovation.


Increase ROI from machine learning with real-time data.


Unify machine learning across the company.

Data Engineers

Spend less time re-writing features for production.


Increase transparency and apply engineering processes to data science.


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