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January 08

17:00 UTC   Start Times Around the World

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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

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.

All Sessions

November 2 2021 15:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
October 12 2021 15:00:00 UTCOML feature highlight: Time Series analysis with Oracle Machine Learning
October 5 2021 15:00:00 UTCOML4Py: Using third-party Python packages from Python, SQL and REST
September 28 2021 15:00:00 UTCWeekly Office Hours: OML on Autonomous Database - Ask & Learn
September 21 2021 15:00:00 UTCOML 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
March 11 2021OML feature highlight: OML Services on Autonomous for Model Deployment
March 2 2021Weekly Office Hours: OML on Autonomous Database - Ask & Learn