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March 11, 2020

Oracle Machine Learning Notebooks - Deep Dive
This is Part I of the deeper dive into Oracle Machine Learning Notebooks. In this Session we learned about administrative and collaborative functionality from the OML Notebooks, which is the Zeppelin–based interface available for the Oracle Autonomous Database.

For Part II we will check more specific and advanced OML4SQL API functionality, including partitioned models and text mining.

We reviewed demos and code, and learned what's possible today, as well as heard about what's coming on Oracle Machine Learning Notebooks for Autonomous Database.

The Slides used in the presentation can be found in the Resources section below.

Video Highlights
01:46 Quick overview of Oracle Machine Learning Notebooks
03:55 List of Topics for Sessions from Part I and Part II
05:00 Quick Orientation Demo
07:06 Object and Relationships
09:55 Users from ADMIN User Administration
12:30 Data Science Team Workspace Permissions
14:04 User Roles from DevTest Workspace Permissions
15:11 Projects and Workspaces
18:44 More on Workspace Permissions
20:07 A Few Notebook Basics - Create/Import/ScratchPad/Version/Save
36:13 Collaboration
37:53 Connection Groups and Interpreter Bindings
38:13 Connection Groups
42:20 Interpreters and Interpreter Bindings
45:10 Override Interpreter Bindings within a Paragraph
47:47 Interpreter Bindings
49:44 Q&A

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Experts

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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.
Marcos Arancibia
Marcos Arancibia, Product Manager, Data Science and Big Data    
Marcos Arancibia is the Product Manager for Oracle Data Science and Big Data. He works with Machine Learning in the Oracle Database and on Big Data clusters under Hadoop and Spark, on premises and in the Oracle Cloud. He works within Product Management to develop product strategy, roadmap prioritization, product positioning and product evangelization, working closely with the engineering team in defining the product roadmaps for Oracle Machine Learning and Big Data in the Cloud. Before joining Oracle 9 years ago he was at SAS Institute Inc. for 13 years as a Data Mining architect and expert in the US and Latin America. He holds a Bachelor Degree of Science in Statistics with additional courses in the Master of Science in Statistics, both from UNICAMP in Brazil. He has Certifications from Stanford on AI and Machine Learning, and from the University of Washington on Computational Neuroscience. He is an expert on Deep Learning and passionate about Machine Learning.