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January 08

17:00 UTC   Start Times Around the World

Oracle Machine Learning for Python, with Demos
We learned about the Oracle Machine Learning for Python and how it integrates the advantages of Python with the scalability and performance of Oracle Database while enabling Python functionality on database data as if they were native Python objects.

Users transparently move from Python functions written for single core to overloaded functions leveraging database parallelism and scalability. Oracle Machine Learning for Python allows users to manipulate data in Oracle Database tables and views using Python syntax and functions, but translating Python functionality into SQL for in-database execution.

Users develop and operationalize comprehensive scripts for analytical applications without leaving the Python environment. Directly integrate user-defined Python scripts into applications and dashboards by immediately invoking Python scripts from SQL. This drastically reduces time-to-market by eliminating porting Python code and developing custom infrastructure, while enabling immediate updates to application code.

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

Video highlights:
03:30 Oracle Machine Learning for Python Introduction
06:50 Traditional Python and Database Interaction
09:40 OML4Py Features
13:20 Demo of OML4Py in Zeppelin Notebooks
16:36 Demo of AutoML - Automatic Algorithm Selection
20:44 Demo of AutoML - Automatic Feature Selection
22:32 Demo of AutoML - Automatic Hyperparameter Tuning
26:55 Demo of In-Database Machine Learning algorithms
35:30 Demo of Transparency Layer
40:52 Demo of Overloaded Data Visualization functions
45:05 Demo of Data Stores
48:35 Demo of Embedded Python Execution
55:58 Demo of Creating user-defined functions in the Python repository
57:10 Demo of Scoring data and Building Models in parallel
1:12:50 Demo of returning Images from Embedded Python execution
1:15:40 Demo of SQL Developer creating and invoking Python scripts via SQL and PL/SQL
1:18:08 Q&A

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Experts

Your Experts
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.
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.