Quick Index for the Learning Resources

The Learning Center has many success resources to help you at all stages during your machine learning journey. This page provides a quick overview index of the material. (If you see something that's missing from our lists here, please click Comment and let us know!)

Types of content for learning resources


  1. Quick Takes
  2. Use Case Demonstrations
  3. Data Prep
  4. Importing Data
  5. Exploratory Data Analysis
  6. Modeling Options
  7. Comparing Models
  8. Investigating Models
  9. Deploying Models
  10. MLOps
  11. API Access (R/Python)
  12. Applications
  13. FAQs

1. Quick Takes

2. Use Case Demonstrations

Overview of using DataRobot to train models on your data and score new data.

3. Data Prep (DataRobot Paxata)

Provides best practices and other helpful information for preparing data for machine learning. (Also see the Data Management Getting Started Guide.)

Best practice series
Data prep insights and helpful tips

4. Importing Data

Explains how to pull data into DataRobot for modeling as well as how much data preparation DataRobot requires for modeling.

5. Exploratory Data Analysis

Shows how to explore your data while understanding the automation and guardrails DataRobot has in place.

6. Modeling Options

Focuses on the processes for modeling setup (such as partitioning) that precede the building of models.

7. Comparing Models 

DataRobot’s automation builds many models. This section explains tools for comparing models.  This includes building models at different sample sizes, feature lists, and ensembling models.

8. Investigating Models 

DataRobot offers many tools for evaluating your model and for explaining how the model works.  This section covers evaluating the overall accuracy of a model as well as interpretability/explainability tools within DataRobot.

9. Deploying Models 

Deployment is a critical component to gaining real value from a model. DataRobot offers many ways to deploy a model.

10. MLOps

11. API Access (R/Python) 

DataRobot is available to use programmatically through our API. Advanced data scientists prefer this approach for integration with other data science tools as well as for setting up automation pipelines. This set of resources is intended to introduce you to the DataRobot API.

12. Applications

Pre-built application accelerators you can use to quickly create AI applications from deployed models.

13. FAQs

Provides answers to frequently asked questions related to machine learning, from thinking about data to building, evaluating, and deploying models. 

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