Introduction
Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended behavior. The resulting program, consisting of the algorithm and associated learned parameters, is called a trained model.
Note
Use the OpsRamp Google public cloud integration to discover and collect metrics against the Google service.Setup
To set up the OpsRamp Google integration and discover the Google service,
go to Google Integration Discovery Profile and select Ml JOB.
Metrics
| OpsRamp Metric | Metric Display Name | Unit | Aggregation Type | Description |
|---|---|---|---|---|
| google_ml_training_cpu_utilization | CPU utilization | Count | Average | Fraction of the allocated CPU that is currently in use. |
| google_ml_training_memory_utilization | Memory utilization | Count | Average | Fraction of the allocated memory that is currently in use. |
| google_ml_training_accelerator_utilization | Accelerator utilization | Count | Average | Fraction of the allocated accelerator that is currently in use. |
| google_ml_training_accelerator_memory_utilization | Accelerator memory utilization | Count | Average | Fraction of the allocated accelerator memory that is currently in use. |
| google_ml_training_network_received_bytes_count | Network bytes received | Bytes | Average | Number of bytes received by the training job over the network. |
| google_ml_training_network_sent_bytes_count | Network bytes sent | Bytes | Average | Number of bytes sent by the training job over the network. |
Event support
- Supported
- Configurable in OpsRamp Google Integration Discovery Profile.