Machine Learning Operations (MLOps) allows organizations to deploy, manage, monitor, and govern their machine learning models from a single place, regardless of how they were created or where they are located. This empowers stakeholders to seamlessly collaborate on the common goal of scaling and managing trusted machine learning models in production.
In this latest Data Science Central podcast, MLOps Agents: Provide Centralized Monitoring for All Your Production Models, we review how organizations can enable centralized monitoring of machine learning models using MLOps Agents.
Learn how MLOps Agents:
Monitor model behavior, including critical events, performance, and availability.
Capture information then send it to a centralized MLOps server, making it much easier to detect and diagnose issues occurring in any production model.
Track models in your preferred environment, meaning you can centrally monitor models deployed in any location on any infrastructure.
Listen to this podcast to take your first step to creating a center of excellence for your production AI.
Seph Mard, Head of Model Risk, Director of Product - DataRobot
Sean Welch, Host and Producer - Data Science Central