AI Accelerators Library

This accelerator aims to assist DataRobot trial users by providing a guided walkthough of the trial experience. DataRobot suggests that you complete the Flight Delays sample use case in the graphical user interface first, and then return to this accelerator.

Understand all of your models in one place and more easily share your findings

Leverage popular python packages as well as DataRobot's python client  to recreate and augment lift chart visualizations.

Leverage DataRobot's python client to extract predictions and compute custom metrics.

Integrate DataRobot API, Papermill, and MLFlow to automate machine learning making it easier, robust, and easy to share.

Access, understand, and tune blueprints for both preprocessing and model hyperparameters.

Learn how to migrate a deployed model using from one DataRobot cluster to another of the same version.

Using Eureqa algorithm to discover the gravitational constant.

Apply FIRE to your dataset and dramatically reduce the number of features.

Import image files using Spark and prepare them into a data frame suitable for ingest

Leverage the power of machine learning to improve customer retention by  building a churn predictor app using Streamlit and DataRobot.

Call the GCP API and enrich a modeling dataset that predicts customer churn

Customize models on the leaderboard via Composable ML's API, the Blueprint Workshop.

Bring external data from Ready Signal to help augment your time series forecasting accuracy

Build model factories leading to the mandatory requirement to significantly decrease training time.

Leverage open source optimization modules to further tune parameters in DataRobot blueprints.