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Deploy an XGBoost Model using OML Services
In this Session Sherry LaMonica, Principal Member of Technical Staff at Oracle Machine Learning team explained how to train a Python SciKit-learn Xgboost model, properly convert the model to ONNX format and then deploy the model to OML Services on Autonomous Database.

OML Services extends OML functionality to support model deployment and model lifecycle management for both in-database OML models and third-party Open Neural Networks Exchange (ONNX) machine learning models via REST APIs.

The REST API for Oracle Machine Learning Services provides REST API endpoints hosted on Oracle Autonomous Database. These endpoints enable the storage of machine learning models along with its metadata, and the creation of scoring endpoints for the model.

These third-party classification or regression models can be built using tools that support the ONNX format, which includes packages like Scikit-learn and TensorFlow, among several others.

Mar 10, 2022 12:00 PM in Eastern Time (US and Canada)

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