Building a Scalable Machine Learning Model Monitoring System with DataRobot and AWS

With the rise of generative AI, companies continue to invest more in their AI strategies. However, many organizations struggle to build out a complete AI lifecycle, especially when it comes to model monitoring and management. They often find it hard to build an easy-to-manage and scalable machine learning (ML) monitoring system that can work for different ML frameworks and environments.

Maintaining multiple ML models across different teams can be challenging and bottleneck the whole AI Production lifecycle. Having a centralized platform to monitor, manage, and govern all of the AI assets can significantly reduce operational overhead and improve efficiency, while decluttering the tech stack.

This white paper highlights the robust AI Production capabilities of the DataRobot AI Platform by showcasing how it can be used to scalably and efficiently monitor models trained and deployed in Amazon SageMaker.

Download this white paper to learn about:

• The high-level architecture for monitoring Amazon SageMaker models in DataRobot
• The step-by-step process of linking a SageMaker-deployed model to DataRobot
• The robust model monitoring capabilities available in DataRobot (model drift, model health, etc).