This set of notebooks from the OML4Py workshop Introduction to Oracle Machine Learning for Python on Autonomous Database introduces you to Oracle Machine Learning for Python (OML4Py) on Oracle Autonomous Database.
Oracle Machine Learning for Python (OML4Py) supports scalable in-database data exploration and preparation using native Python syntax, scalable in-database algorithms for machine learning model building and scoring, and automated machine learning (AutoML). Users can also invoke user-defined Python functions from Python and REST APIs using database-spawned Python engines. OML4Py increases data scientist productivity and reduces solution deployment complexity. Join us for this tour of OML4Py.
Python is a major programming language used for data science and machine learning. OML4Py is a feature on Oracle Autonomous Database that provides Python users access to powerful in-database functionality supporting data scientists for both scalability, performance, and ease of solution deployment.
Oracle Machine Learning Notebooks is a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database.
Key Features:
- Collaborative UI for data scientists
- Enables sharing of notebooks and templates with permissions and execution scheduling
- Access to 30+ parallel, scalable in-Database implementations of machine learning algorithms
- Python, SQL and PL/SQL scripting language supported
- Enables and supports deployments of enterprise machine learning methodologies in Autonomous Data Warehouse (ADW), Autonomous Transactional Database (ATP) and Autonomous JSON Database (AJD)
The current folder contains the examples based on Oracle Machine Learning for Python (OML4Py) used for the Live Labs "Python Users: Build intelligent applications faster with Oracle Machine Learning":
- Lab 1 Getting Started with OML4Py - Basic Introduction to the OML Notebooks environment and layout
- Lab 1a Run Me First OML4Py table creation and grants - please execute this notebook first, since it contains tables and views used by the Labs
- Lab 2 OML4Py Transparency Layer - Examples on how to use Transparency Layer from Python
- Lab 3 OML4Py Algorithms - Examples of using Oracle Machine Learning Algorithms from Python
- Lab 4 OML4Py Datastore - Examples of storing Python scripts and objects into Oracle Database (OML4Py Datastore)
- Lab 5 OML4Py Embedded Python Execution - Examples of using open-source Python scripts and algorithms (like SciKit-Learn) with OML4Py
- Lab 6 OML4Py AutoML - Examples of how to execute OML AutoML processes to identify the ideal algorithms and tune the models.
More information on the Live Labs workshop Introduction to Oracle Machine Learning for Python on Autonomous Database
See also Announcing next generation OML Notebooks on Oracle Autonomous Database blog post for more information on OML Notebooks.
Last updated: April 2023