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.
In addition, OML Services supports proprietary cognitive text capabilities, with capabilities for topic discovery, keywords, summary, sentiment, and feature extraction. The initial languages supported include English, Spanish, French and Italian (based on a Wikipedia knowledgebase using embeddings).
OML Services cognitive image functionality, supported through the ONNX format third-party model deployment feature, supports scoring using images or tensors.