Introduction

Pages in this section give you a walkthrough of the typical end-to-end workflow on the AI & Analytics Engine. You will learn to:

  1. Create a new organization
  2. Create your first project in the new organization
  3. Create a dataset in a project by uploading a CSV file stored on your computer
  4. Build a recipe to prepare your data for model training
  5. Create an application from data
  6. Train a model
  7. Compare models and deploy
  8. Obtain predictions from deployed models

This quick-start guide uses the Statlog (German Credit Data) dataset that is open for public access, provided by OpenML. It can be downloaded from here. We want to use this dataset to train a model that can predict whether account holders with given attributes will exhibit "good" or "bad" behavior.

Navigate to the appropriate page below, depending on the access type you prefer to work with:

  1. Using the Graphical User Interface (GUI)
  2. Using SDK-aided API calls