Run your existing models as APIs or scheduled jobs. No black boxes.
Rely on enterprise-grade infrastructure to confidently deploy your work.
Manage your models’ lifecycles by tracking scoring, output, and drift.
Deploy Python, R, or Spark code behind a REST API to make it instantly available for integration with real-time applications or dashboards.
Run code on a schedule to automate data science processes like data cleaning and model retraining.
Measure model performance and its impact on your business with data from API calls, training, and cross validation.
Test multiple models at once, promote new models into production, and continuously iterate over time.