Hands-On Lab using Oracle Machine Learning AutoML UI on Autonomous Database

In this Hands on Lab, we experienced Oracle Machine Learning AutoML UI on Oracle Autonomous Database.
AutoML UI provides the features of OML Automated Machine Learning (AutoML) for algorithm selection, adaptive sampling, feature selection and hyperparameter tuning.
AutoML UI allows for an automatic creation of a OML4Py Notebook with content for the best tuned model and all hyperparameters chosen by AutoML for the model desired.
AutoML UI also deploys Models to OML Services with one click, which creates REST APIs for the native in-database OML models and makes them ready to score in real-time.
Sign up for this tour of OML AutoML UI, and we will distribute credentials for you to do the Live exercises using the environment during the Session.
Video Highlights:
00:33 Goals for the HOL Session
01:20 Expectations for the HOL Session
02:10 Agenda
03:35 Accessing the Live Labs Instance
09:55 Introduction to OML AutoML UI
15:38 Performance considerations for OML AutoML UI
17:38 OML expected Workflow
19:36 Preparing the Live Labs environment
21:30 Labs overview
22:47 Lab 1 - Access OML Notebooks and create your first model using OML AutoML UI
40:38 Lab 1 - Q&A
43:30 Lab 2 - Create an auto-generated OML Notebook from your first model
49:55 Lab 2 - Bonus Rounds - additional Prediction and Probabilities in OML4Py
54:37 Lab 3 - Deploy and AutoML UI model to REST API on OML Services
59:08 Lab 4 - Create a second Experiment with more models and Recall model metric
1:06:43 Lab 5 - Run AutoML using OML4Py as a comparison
1:14:59 Lab 6 - Bonus Section: Use Postman to access OML Services REST APIs to score the OML AutoML UI model deployments
1:16:38 Where to go from here?
1:17:37 Q&A