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:
Ranking & search
Inventory / Supply chain
Work smarter, together
Share ideas and iterate on features.
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.
Understand how features are computed and encoded and explore data sources.
Add value to machine learning across your organization
Own the feature lifecycle from idea to production.
Visualize features and drill into data with a few clicks.
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.
Spend less time re-writing features for production.
Increase transparency and apply engineering processes to data science.
Contact us at firstname.lastname@example.org