Where does DataRobot fit within the augmented analytics segment?
DataRobot’s “automated machine learning” is closely related to Gartner’s “augmented” analytics.
“Augmented analytics will enable expert data scientists to focus on specialized problems and on embedding enterprise-grade models into applications. Users will spend less time exploring data and more time acting on the most relevant insights with less bias than is the case with manual approaches.” says Gartner.
Gartner splits augmented analytics into 3 sub-categories:
1. Augmented Data Preparation is defined as the use of machine learning to enhance and enrich data. DataRobot has several features that fit within this sub-category, including missing value imputation, target leakage detection, time series feature generation, and importing external calendars.
2. Augmented Data Discovery is defined as the use of machine learning to enable citizen data scientists to find, visualize and narrate findings, without having to manually build models or write algorithms. Since the time it was first released, DataRobot has automatically generated data insights such as exceptions, segments, links and predictions. After Gartner’s report was first published, DataRobot released its new automated model documentation capabilities, which automatically writes a detailed narrative of the data insights, algorithms and results, suitable for regulatory reporting purposes.
3. Augmented Data Science and Machine Learning is defined as the automation of key aspects of advanced analytic modeling, to reduce the requirement for specialized skills to generate, operationalize and manage models. These are all core skills for DataRobot, the pioneer of automated machine learning, which automatically chooses algorithms, selects features, deploys models and monitors deployed models to pro-actively identify when models need to be refreshed.
For more information, visit: https://blog.datarobot.com/what-is-augmented-analytics-and-where-does-datarobot-fit-in