1 Getting Started with AI in Oracle Monetization Suite

Learn how to get started with artificial intelligence (AI) using the deployment assets provided for Oracle Monetization Suite. These assets include container images, Helm charts, configuration files, and other components that assist you in installing, deploying, and managing AI services on Kubernetes. You can use these resources in both Oracle Cloud Infrastructure (OCI) and other supported environments.

Topics in the document:

• Overview of the Deployment Package

• Tasks for Preparing Your Cloud Native Environment

• Downloading Packages for the Cloud Native Helm Charts and Docker Files

• Pulling Images from the Oracle Container Registry

• Downloading Images from Oracle Software Delivery Website

• Monitoring AI Services in Cloud Native Environment

• Configuring Grafana for AI Services Metrics

Overview of the Deployment Package

The deployment package provides all components required to deploy, manage, and monitor data, training, and prediction services for AI-based workflows on Kubernetes. The package includes:

• Ready-to-use Docker images and Helm charts for each service, allowing you to orchestrate containers in your Kubernetes environment. Update the values.yaml file as needed to reflect your specific configuration requirements.

• Dockerfiles and build files that enable you to apply minor updates to the base operating system or add additional dependencies.

• Sample configuration files, Python scripts, and shell scripts that serve as references for customizing your deployment framework.

• Templates for Kubernetes persistent storage, which you can modify to suit your application data and artifact storage needs.

• Sample Grafana dashboard JSON files for each service, designed to streamline performance monitoring and observability with Prometheus and Tempo.

Tasks for Preparing Your Cloud Native Environment

Prepare your system for the BRM cloud native deployment by performing the following high-level tasks:

1. Downloading the Helm charts for the cloud native deployment. See "Downloading Packages for the Cloud Native Helm Charts and Docker Files".

2. Downloading the cloud native images in one of these ways:

   1. From the Oracle Container Registry. To do so, see "Pulling Images from the Oracle Container Registry".

   2. From the Oracle Software Delivery website. To do so, see "Downloading Images from Oracle Software Delivery Website".

Downloading Packages for the Cloud Native Helm Charts and Docker Files

To download the packages for the cloud native helm charts and docker files, see "Downloading Packages for the BRM Cloud Native Helm Charts" in BRM Cloud Native Deployment Guide.

Table 1-1 lists the package name and archive files that need to be extracted for AI and ML services.

Table 1-1 Packages and Archive Files

Package Name	Archive Files
Oracle Communications Cloud Native ML Helm Charts 15.2.0	ml-helmcharts.tgz Includes: data-fetch-15.2.0.0.0.tgz train-utility-15.2.0.0.0.tgz recommendation-15.2.0.0.0.tgz
Oracle Communications Cloud Native ML Docker files and Artifacts Bundle 15.2.0	ml-dockerfiles-artifacts.tgz Includes: ml-data-fetch-orchestrator-artifacts.zip ml-data-fetch-processor-artifacts.zip ml-train-utility-orchestrator-artifacts.zip ml-train-utility-processor-artifacts.zip ml-recommendation-orchestrator-artifacts.zip ml-recommendation-predictor-artifacts.zip

Pulling Images from the Oracle Container Registry

To pull images from the Oracle Container Registry, see "Pulling BRM Images from the Oracle Container Registry" in BRM Cloud Native Deployment Guide.

Table 1-2 lists the image names for the AI-ML cloud native components.

Table 1-2 Cloud Native Images

Component Name	Image Name
ML Data Fetch Orchestrator	ml-data-fetch-orchestrator
ML Data Fetch Processor	ml-data-fetch-processor
ML Train Utility Orchestrator	ml-train-utility-orchestrator
ML Train Utility Processor	ml-train-utility-predictor
ML Recommendation Orchestrator	ml-recommendation-orchestrator
ML Recommendation Predictor	ml-recommendation-predictor

Downloading Images from Oracle Software Delivery Website

To download images from Oracle Software Delivery Website, see "Downloading BRM Images from the Oracle Software Delivery Website" in BRM Cloud Native Deployment Guide.

Table 1-3 lists the names of the packages and package files that you need to extract for using and configuring AI and ML services.

Table 1-3 Cloud Native Package and Package Files

Package Name	Package File Name
Oracle Communications Cloud Native ML Data Service Images 15.2.0	ml-data-fetch.tgz Includes: ml-data-fetch-orchestrator.tar ml-data-fetch-processor.tar
Oracle Communications Cloud Native ML Training Service Images 15.2.0	ml-train-utility.tgz Includes: ml-train-utility-orchestrator.tar ml-train-utility-processor.tar
Oracle Communications Cloud Native ML Recommendation Service Images 15.2.0	ml-recommendation.tgz Includes: ml-recommendation-orchestrator.tar ml-recommendation-predictor.tar

Monitoring AI Services in Cloud Native Environment

You can monitor all the AI services using various service metrics. These metrics allows you to track and monitor the total number of requests and the time taken by the services.

Table 1-4 lists the available metrics for the services along with their descriptions.

Table 1-4 Service Metrics

Metric Name	Description
aiml_data_service_request_seconds	Total time taken by the previous data service request.
aiml_data_service_request_seconds_count	Total number of data service requests received.
aiml_data_service_request_seconds_max	Maximum time taken by a data service request to process.
aiml_data_service_request_seconds_sum	Total time taken by all the data service requests to finish processing.
aiml_train_service_request_seconds	Total time taken by the previous training service request.
aiml_train_service_request_seconds_count	Total number of training service requests received.
aiml_train_service_request_seconds_max	Maximum time taken by a training service request to process.
aiml_train_service_request_seconds_sum	Total time taken by all the training service requests to finish processing.
recommend_service_request_seconds	Total time taken by the previous prediction service request.
recommend_service_request_seconds_count	Total number of prediction service requests received.
recommend_service_request_seconds_max	Maximum time taken by a prediction service request to process.
recommend_service_request_seconds_sum	Total time taken by all the prediction service requests to finish processing.

In addition to these, all the metrics that are provided by Micrometer, Tempo, and Kubernetes are available.

Configuring Grafana for AI Services Metrics

You can create a dashboard in Grafana to display the metric data for your AI services.

Alternatively, you can use the sample dashboards included in the ml-helmcharts.tgz package.

Table 1-5 lists each sample dashboard.

Table 1-5 Sample Grafana Dashboards

File Name	Description
data-fetch-grafana-dashboard.json	Allows you to view metrics for the data service requests.
train-utility-grafana-dashboard.json	Allows you to view metrics for the training service requests.
recommendation-grafana-dashboard.json	Allows you to view metrics for the prediction or recommendation service requests.