On this weekly Office Hours for Oracle Machine Learning on Autonomous Database, Jie Liu, Data Scientist for Oracle Machine Learning, covered Cross-Validation methods, why they are useful and how to run it using in-Database methods using OML and also Embedded Python Execution open-source methods. He also presented a demo running on a notebook with OML4Py.
The Oracle Machine Learning product family supports data scientists, analysts, developers, and IT to achieve data science project goals faster while taking full advantage of the Oracle platform.
The Oracle Machine Learning Notebooks offers an easy-to-use, interactive, multi-user, collaborative interface based on Apache Zeppelin notebook technology, and support SQL, PL/SQL, Python and Markdown interpreters. It is available on all Autonomous Database versions and Tiers, including the always-free editions.
OML includes AutoML, which provides automated machine learning algorithm features for algorithm selection, feature selection and model tuning, in addition to a specialized AutoML UI exclusive to the Autonomous Database.
OML Services is also included in Autonomous Database, where you can deploy and manage native in-database OML models as well as ONNX ML models (for classification and regression) built using third-party engines, and can also invoke cognitive text analytics.