Governing a deployed deep learning blueprint

In this section, we will discuss how DataRobot enables users to govern their deep-learning models effectively by providing comprehensive tools for model utilization, monitoring, and maintenance. With a focus on seamless integration, DataRobot allows users to deploy AI applications on cloud-based or on-premises infrastructure, manage prediction outputs, and monitor model performance using custom metrics and alerts. Furthermore, the platform supports data drift detection and offers retraining capabilities for continuous model improvement. We will explore these features in detail, demonstrating how DataRobot empowers users to efficiently manage their deep learning models and ensure optimal performance throughout their life cycle.

Governing through model utilization in DataRobot

Users can access their models through various means, such as API calls, Python interfaces, or DataRobot-made applications called AI Apps. The platform supports...