Automate machine-learning model monitoring with New Relic One and DAGsHub

Tracking machine-learning (ML) models can be time-consuming, costly, and difficult. Comparing the performance of models in production to ensure more relevant and meaningful results across versions and datasets is a major challenge unless you have automated status checks and real-time metrics. Imagine a world where you can seamlessly integrate experiment tracking and ML flow, with visibility into model performance.

New Relic is partnering with DAGsHub to help data scientists and machine learning engineers automatically monitor, visualize, analyze, and synchronize model experiments through machine learning. The DAGsHub integration with New Relic One will allow you to set alerts to get automated status checks, create custom visualizations through ad-hoc custom metrics, and track your performance in real-time.

In the following short demo, you'll learn how to integrate DAGsHub data in New Relic One, add an alert, analyze your metrics, and build relevant customized dashboards where you can visualize your model training and evaluation metrics in real-time.