New Relic Extends Full-Stack Observability to ML with Model Performance Monitoring
December 08, 2021
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New Relic is extending its observability experience to provide a new offering for artificial intelligence (AI) and machine learning (ML) teams to break down visibility silos.

This brand new offering provides AI/ML and DevOps teams one place to monitor and visualize critical signals like recall, precision, and model accuracy alongside their apps and infrastructure.

AI/ML engineers and data scientists can now send model performance telemetry data into New Relic One and—with integrations to leading machine learning operations (MLOps) platforms—proactively monitor ML model issues in production. You can empower your data teams with full visibility, with custom dashboards and visualizations that can show you the performance of your ML investments in action.

AI and ML models are based on both code and the underlying data. Because the real world is constantly changing, models developed on static data can become irrelevant or “drift” over time, becoming less accurate. Monitoring the performance of an ML model in production is essential to continue to deliver relevant customer experiences.

By using New Relic One for your ML model performance monitoring, your development and data science teams can:

- Bring your own ML data or integrate with data science platforms and monitor ML models and interdependencies with the rest of the application components, including infrastructure, to solve problems faster.

- Create custom dashboards to gain trust and insights for more accurate ML models.

- Apply predictive alerts to ML models from New Relic Alerts and Applied Intelligence to detect unusual changes and unknowns early before they impact customers.

- Review ML model telemetry data for critical signals to maintain high-performing models.

- Collaborate in a production environment and contextualize alerts, notifications, and incidents before they have an impact on the business.

- Access data that allows you to make data-driven decisions, such as boosting innovation, planning decisions, increasing reliability, and enhancing customer experience.

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