Platform Availability
OML offers flexibility by supporting deployment across various platforms.
The key deployments are:
- Oracle Autonomous Database (ADB)
- Base Database Service (BDBS)
- On-Premises Oracle Database
Oracle Machine Learning Family of Components
The following table describes the availability of OML components on different platforms.
| OML Component | Autonomous Database Serverless | Dedicated Region | Autonomous Database on Dedicated Exadata Infrastructure | Cloud @ Customer | Oracle Database On premises, Base Database Service, Cloud Service, Cloud Infrastructure, Cloud@Customer |
|---|---|---|---|
|
OML4SQL API Build machine learning models and score data with no data movement using SQL and PL/SQL |
✓ |
✓ |
✓ |
|
OML4Py API Leverage the database as a high performance compute engine from Python with in-database machine learning |
✓ |
✕ |
✓ |
|
OML4R API Leverage the database as a high performance compute engine from R with in-database machine learning |
✓ |
✕ |
✓ |
|
OML Notebooks SQL, PL/SQL, Python, R, Conda, and markdown interpreters |
✓ |
✕ |
✕ |
|
OML AutoML UI No code automated modelling interface |
✓ |
✕ |
✕ |
|
OML Monitoring No-code user interface for monitoring changes in data and in-database machine learning model quality |
✓ |
✕ |
✕ |
|
OML Services RESTful model management, deployment, monitoring |
✓ |
✕ |
✕ |
|
Oracle Data Miner SQL Developer extension with a drag-n-drop interface for creating machine learning methodologies |
✓ |
✓ |
✓ |
Supported operating systems includes Linux operating system and Windows Server versions. See Machine Learning System Requirements.
Parent topic: Components