Integrate Oracle Analytics with OCI Language

Integrate Oracle Analytics with Oracle Cloud Infrastructure (OCI) Language to perform machine learning and artificial intelligence without needing data scientist expertise. For example, with customer data, you might perform sentiment analysis to analyze reviews that customers have typed into a feedback portal.

Topics:

• Prerequisites for Integrating OCI Language Models With Oracle Analytics

• Policies Required to Integrate OCI Language with Oracle Analytics
• Make an OCI Language Model Available in Oracle Analytics

Prerequisites for Integrating OCI Language Models With Oracle Analytics

You need these prerequisites to integrate OCI Language with Oracle Analytics.

• Create a connection between your Oracle Analytics instance and your OCI service - See Create a Connection to Your OCI Tenancy.

Policies Required to Integrate OCI Language with Oracle Analytics

To integrate Oracle Analytics with OCI Language, make sure that you have the required security policies.

The OCI user that you specify in the connection between Oracle Analytics Cloud and your OCI tenancy must have read, write, and delete permissions on the compartment containing the OCI resources you want to use. Ensure that the OCI user belongs to a user group with the following minimum OCI security policies. When you connect to an OCI tenancy from Oracle Analytics, you can use either an OCI API key or resource principal.

Note: For resource principal, to include all Analytics instances under a compartment, specify {request.principal.type='analyticsinstance', request.principal.compartment.id='<compartmentA_ocid>'} instead of {request.principal.id='<analytics_instance_ocid>'}.

Table 32-6 Security policies required for OCI Language integration

API Key Policies	Resource Principal Policies
Allow group <group_name> to use ai-service-language-family in tenancy	Allow any-user to use ai-service-language-family in tenancy where all {request.principal.id='<analytics_instance_ocid>'}
Allow group <group_name> to read buckets in compartment <compartment_name>	Allow any-user to read buckets in compartment <compartment_name> where all {request.principal.id='<analytics_instance_ocid>'}
Allow group <group_name> to manage objects in compartment <compartment_name> where target.bucket.name='<staging_bucket_name>'	Allow any-user to manage objects in compartment <compartment_name> where all {request.principal.id='<analytics_instance_ocid>', target.bucket.name='<staging_bucket_name>'}
Allow group <group_name> to read objectstorage-namespaces in tenancy	Allow any-user to read objectstorage-namespaces in tenancy where all {request.principal.id='<analytics_instance_ocid>'}

Make an OCI Language Model Available in Oracle Analytics

Before you can use Oracle Cloud Infrastructure (OCI) Language models to analyze data, you register them in Oracle Analytics.

Register OCI Language Models in Oracle Analytics to build key phrase extraction, sentiment analysis, classification, named entity recognition, and language recognition into your applications without requiring artificial intelligence (AI) expertise.

Oracle Analytics supports these models:

• Key phrase extraction
• Language detection
• Name entity recognition
• Sentiment analysis
• Text classification

Note: Oracle Analytics doesn't support custom models created in OCI AI Language.

Before you start, create a connection between your Oracle Analytics instance and your OCI service. See Create a Connection to Your OCI Tenancy.

In addition, make sure that you log into Oracle Analytics as a user with the BI Service Administrator or DV Content Author role.

1. On the Home page, click Page Menu, then Register Model/Function, then OCI Language Models.
2. On the Register a Language Model dialog, click the name of a connection to your OCI tenancy.
3. On the Select a Model dialog, select the model that you'd like to make available in Oracle Analytics.
4. In the pop-up panel, use the Staging Bucket Name field to specify the name of a staging bucket for the model.
   
   
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5. Click Register.
6. Optional: To confirm that the model was registered successfully, from the Home page, click Navigator, click Models, then click Machine Learning to display registered models and confirm that the model was registered successfully. Click Inspect to check that you've configured the model correctly.