Table of Contents
- Title and Copyright Information
- Preface
- 1 What's New
-
2
Get Started
- Get Started with Oracle Machine Learning User Interface
- Access Oracle Machine Learning User Interface
- Oracle Machine Learning User Interface Home Page
- Oracle Machine Learning Notebooks
- GitHub Notebooks
- AutoML UI
- Data Science Agent
- Workflows
- Data Monitoring
- Model Monitoring
- REST API for Oracle Machine Learning Services
- 3 Project and Workspaces
-
4
OML Notebooks
- Oracle Machine Learning Notebooks
- GitHub Notebooks
- Enable GPU Compute Capabilities in a Notebook through the Python Interpreter
-
Visualize your Data in Oracle
Machine Learning Notebooks
- Visualize Data in a Table
- Visualize Data in a Bar Chart
- Visualize Data in a Funnel Chart
- Visualize Data in a Pyramid Chart
- Visualize Data in a Scatter Plot
- Visualize Data in a Line Chart
- Visualize Data in an Area Chart
- Visualize Data in a Pie Chart
- Visualize Data in a Box Plot
- Visualize your Data in a Sunburst Chart
- Visualize your Data in a Tag Cloud
- Visualize your Data in a Treemap
- Visualize Data in a Map
- Export a Notebook
- Import a Notebook
- About Interpreters and Notebook Service Levels
-
Run Notebooks using different
interpreters
- Use the SQL Interpreter in a Notebook Paragraph
- Use the Python Interpreter in a Notebook Paragraph
- Use the R Interpreter in a Notebook Paragraph
-
Use the Conda Interpreter in a Notebook Paragraph
- About the Conda Environment and Conda Interpreter
- Conda Interpreter Commands
- Create a Conda Environment for Python, Install a Python Package and Upload it to Object Storage
- Create a Conda Environment for R and Install R Package and Upload it to Object Storage
- Upload the Conda environments to an Object Storage bucket associated with the Autonomous AI Database
- Use the Markdown Interpreter and Generate Static html from Markdown Plain Text
- Use the Scratchpad
- 5 AutoML UI
- 6 Workflows
-
7
Data Science Agent
- Data Science Agent
- Data Science Agent Concepts
- Key Highlights of Data Science Agent
- Best Practices for using Data Science Agent
- Data Science Agent: Sample Prompts and Outputs
- Prerequisites to use Data Science Agent
- Limitations of Data Science Agent
- Access Data Science Agent
- Create AI Profile and Cloud Credential
- Create an OCI Generative AI Credential and AI Profile
- Manage Data Science Conversations
- Use Data Science Agent Chat Interface
- Example of a Conversation with Data Science Agent
- Known Issues in Data Science Agent
- 8 Data Monitoring
- 9 Model Monitoring
- 10 Models
- 11 Templates
- 12 Jobs
- 13 Admin
-
FAQs
- How to grant access to an OML model by giving global or selective access to other users?
- How to obtain the URL for REST API for OML Services, REST API for OML4Py and OML4R embedded execution from the Autonomous AI Database ?
- How to push content represented by an OML proxy object into a table in the Oracle Autonomous AI Database?
- What is the expected behavior if the embedded REST execution requests surpass the capacity of the VM?
- Who owns and manages the VMs on which embedded Python execution is downloaded?
- Where can I find the list of all the functions available in the oml python package?
- What is the difference between the OML ECPUs and the Database ECPUs?
- What is an alternative solution to oml.connect() for Oracle Machine Learning solutions deployment?
- What other models can I host on OML besides the in-database models?
- Does Oracle Machine Learning support reading of data from OCI Streaming services or Kafka?
- How can I create a Conda environment for Python and R, install packages and upload the environment in a single step
- Troubleshooting