It's important to test how your machine learning (ML) models are working with your software and infrastructure, but you can only do so if you have the right tools in place.
That’s why New Relic is partnering with Amazon SageMaker to integrate ML model telemetry into New Relic One, providing an end-to-end observability platform where you can monitor the performance of your AI-powered applications.
The integration with Amazon SageMaker expands access to observability for ML engineers and data science teams, making it easier to develop, test, and monitor ML models in production by breaking down the silos between AI/ML, DevOps, and site reliability engineers (SREs).
Bridging the gap between data science and DevOps
By integrating SageMaker metrics in New Relic One, you unlock the ability to instrument, analyze, troubleshoot, and optimize your machine-learning application performance to ensure production models deliver accurate predictions.
Gain trust in your AI-powered solutions with New Relic Alerts and Applied Intelligence, a centralized notification system to monitor all your operational needs to maximize your data science ROI.
New Relic (MLOps) Model Performance Monitoring and Amazon SageMaker enables data science teams to measure ML model performance to maximize your business impact.
Integrating Amazon SageMaker Model Monitor with New Relic One
By monitoring models developed with Amazon SageMaker in New Relic One, you can now visualize sophisticated ML models and create a comprehensive monitoring dashboard in New Relic for your ML models and applications pipelines.
The following video shows how to integrate metrics from Amazon SageMaker Model Monitor into New Relic One.
You can also follow these steps to set up the integration.
- Stream metrics: Stream AWS Cloudwatch metrics to New Relic with Terraform or by following along with the Amazon CloudWatch Metric Streams integration docs.
- Monitor your data: Automatically monitor your data and model in Amazon Sagemaker and send metrics to Cloudwatch.
- Explore entities and dashboards: Explore your MLOp entities and dashboards by selecting Explorer in one.newrelic.com and selecting Machine Learning from the left navigation menu.
For more information on setting up New Relic Model Performance Monitoring and Amazon SageMaker in your observability infrastructure, visit the MLOps integrations on New Relic Instant Observability.
Next steps
Learn more about New Relic MLOps.
And if you’re new to New Relic but interested in digging in, experience the simplicity of New Relic One yourself by signing up for a forever free account.
The views expressed on this blog are those of the author and do not necessarily reflect the views of New Relic. Any solutions offered by the author are environment-specific and not part of the commercial solutions or support offered by New Relic. Please join us exclusively at the Explorers Hub (discuss.newrelic.com) for questions and support related to this blog post. This blog may contain links to content on third-party sites. By providing such links, New Relic does not adopt, guarantee, approve or endorse the information, views or products available on such sites.
