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May 25

15:00 UTC   Start Times Around the World

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Description

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

Your Experts

Marcos Arancibia
Marcos Arancibia, Senior Principal Product Manager, Machine Learning    
Marcos Arancibia is the Product Manager for Oracle Machine Learning, working with Machine Learning in the Oracle Database and on Spark. He develops product strategy, roadmap prioritization, product positioning and product evangelization, helping define the product roadmap for Oracle Machine Learning. Before joining Oracle in 2010 he spent 13 years at SAS Institute Inc., from Country Manager in LAD to Regional Data Mining lead in the US. He holds a bachelor's degree with additional courses in the master's degree, both in Statistics from UNICAMP in Brazil. He has Certifications from Stanford on AI and Machine Learning, and from the University of Washington on Computational Neuroscience.
Mark Hornick
Mark Hornick, Senior Director, Product Management, Data Science and Machine Learning    
Mark Hornick is the Senior Director of Product Management for the Oracle Machine Learning (OML) family of products. He leads the OML PM team and works closely with Product Development on product strategy, positioning, and evangelization, Mark has over 20 years of experience with integrating and leveraging machine learning with Oracle technologies, working with internal and external customers in the application of Oracle’s machine learning technologies for scalable and deployable data science projects. Mark is Oracle’s representative on the R Consortium’s Board of Directors, an Oracle Adviser and founding member of the Business Intelligence Warehousing and Analytics (BIWA) User Community, and Content Selection Committee Chair for the Analytics and Data Summits.
Sherry LaMonica
Sherry LaMonica, Principal Member of Technical Staff, Oracle Machine Learning Product Management
Sherry is a member of the Oracle Machine Learning Product Management team. She has 20 years of software experience focused on enabling the commercial use of the open-source data analysis software system R for data science projects. She has worked with customers in fields as diverse as as pharmaceutical research, financial analysis, manufacturing and healthcare IT.

All Sessions

November 30 2021 16:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
November 23 2021 16:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
November 16 2021 16:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
November 9 2021 16:00:00 UTCOML Usage Highlight: Leveraging OML algorithms in Retail Science platform - Fraud Detection
October 12 2021OML feature highlight: Time Series analysis with Oracle Machine Learning
October 5 2021OML4Py features: Using third-party Python packages from Python, SQL and REST
September 21 2021OML usage highlight: Live Demo of Oracle Stream Analytics with OML AutoML UI and OML Services
August 17 2021OML Usage Highlight: ML on SailGP data: Predicting the best sailing direction
August 10 2021OML feature highlight: Deploy an XGBoost Model using OML Services
August 3 2021ML Concepts - Using Cross-Validation with OML in-Database and with Embedded Python Execution
June 29 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn
June 22 2021ML Concepts - Encoding of Categorical Attributes: OneHot vs Mean vs WoE and when to use them
June 15 2021OML usage highlight: Machine Learning Recommendations for Maintenance and Repair
May 25 2021Hands-On Lab using Oracle Machine Learning AutoML UI on Autonomous Database
May 18 2021Hands-On Lab using Oracle Machine Learning Services on Autonomous Database
May 11 2021OML usage highlight: Oracle Process Automation with Real-time OML Services scoring
April 20 2021OML usage highlight: Oracle Stream Analytics with Real-time OML Services scoring
April 13 2021OML usage highlight: Making Oracle Digital Assistant smarter with OML Services
March 30 2021OML feature highlight: OML AutoML UI for Automated Model Building
March 23 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn