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DagsHub and New Relic - Real-time ML training monitoring | DagsHub's New Relic integration lets you monitor ML training metrics in real time, log experiments with MLflow, create custom metrics, and set alerts. |
New Relic{:rel="nofollow" target="_blank"} is one of the most prominent observability platforms. Hundreds of companies trust New Relic to gather and analyze data and respond to incidents in real-time. DagsHub lets you connect to New Relic to monitor and analyze machine learning training metrics. You can log experiments using MLflow, create custom metrics, and set alerts for them.
To log experiments to New Relic in real-time, you must first log metrics in real-time to DagsHub. This is done using MLflow. To learn how to log metrics to DagsHub using MLflow, please read the MLflow docs on DagsHub.
DagsHub forwards training metrics, logged by MLflow, to your New Relic account using API calls. This lets you analyze your data using NRQL, and turn on customized alerts. To learn how to integrate DagsHub with New Relic and monitor machine learning training metrics in real-time, please read the DagsHub and New Relic integration documentation.
By connecting DagsHub to New Relic, you get a fully configured dashboard that shows your training and evaluation metrics in real-time.
 DagsHub dashboard on New Relic```sql
SELECT latest(<metric_name>) FROM Metric TIMESERIES SINCE 1800 seconds ago
```
Some metrics are lineary dependent, but they give a better grasp of the data than looking at a set of raw orthogonal metrics. You might want to look at squared values or absolute values for instance. Logging those metrics seems like data duplication, and often you will manipulate your metrics postmortem. The query language used to fetch your metrics is called NRQL, and it can be leveraged to calculate custom metrics out of your raw metrics. You can log orthogonal metrics, and monitor any metric created out of them in real-time, as long as it's computable using NRQL.
 MetricsThe process to monitor a custom metric is the same as monitoring a metric, besides the query itself. Let's monitor the squared value of our metric named "Loss Objectness".
```sql
SELECT latest(loss_objectness)*latest(loss_objectness) as loss_objectness_squared FROM Metric TIMESERIES SINCE 1800 seconds ago
```
You can use alerts to receive notifications that a metric reached a specific value or fills a certain condition. The simplest way to set an alert is from an existing metric on your dashboard.
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