Addressing data science challenges with DataRobot

Now that you know what DataRobot offers, let's revisit the data science process and challenges to see how DataRobot helps in addressing these challenges and why this is a valuable tool in your toolkit.

Lack of good-quality data

While DataRobot cannot do much to address this challenge, it does offer some capabilities to handle data with quality problems:

• Automatically highlights data quality problems.
• Automated EDA and data visualization expose issues that could be missed.
• Handles and imputes missing values.
• Detection of data drift.

Explosion of data

While it is unlikely that the increase in the volume and variety will slow down any time soon, DataRobot offers several capabilities to address these challenges:

• Support for SparkSQL enables the efficient pre-processing of large datasets.
• Automatically handles categorical data encodings and selects appropriate model blueprints.
• Automatically...