Data¶
Data integrity and quality are cornerstones for creating highly accurate predictive models. These sections describe the tools and visualizations DataRobot provides to ensure that your project doesn't suffer the "garbage in, garbage out" outcome.
| Topic | Describes... |
|---|---|
| Fundamentals of working with data | Learn how DataRobot's data prep, management, and transformation tools support your ML workflow. |
| Connect to data sources | Set up database connections using a “self-service” JDBC platform. |
| AI Catalog | Import data into the AI Catalog and from there, you can transform data using SQL, as well as create and schedule snapshots of your data. Then, create a DataRobot project from a catalog asset. |
| Import data | Import data from a variety of sources. |
| Transform data | Transform primary datasets and perform Feature Discovery on multiple datasets. |
| Analyze data | Investigate data using reports and visualizations created after EDA1 and EDA2. |
| Pipelines | Integrate a set of connected data processing steps to train models with new data as needed. |
| Reference | |
| Dataset requirements | Dataset requirements, data type definitions, file formats and encodings, and special column treatments. |
Updated June 15, 2022
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