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October 05, 2021

15:00 UTC   Start Times Around the World

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OML4Py features: Using third-party Python packages from Python, SQL and REST
Join us on this Office Hours for Oracle Machine Learning on Autonomous Database, where Sherry LaMonica, Principal Member of Technical Staff at Oracle Machine Learning team will explain how to use third-party Python packages from Python, SQL and REST via OML4Py.

So you want to deploy your Python scripts to production in the database environment. With Oracle Machine Learning for Python, you can invoke user-defined functions on database-spawned and controlled Python engines using embedded Python execution. An added benefit is the ability to easily leverage data-parallel and task-parallel invocation aided by the database environment. Join this session to learn about the options for working with third-party packages with OML4Py, both on premises and in Autonomous Database, from the Python, SQL and REST APIs.

The Oracle Machine Learning product family supports data scientists, analysts, developers, and IT to achieve data science project goals faster while taking full advantage of the Oracle platform.

01:05 Goals for today
01:24 Third-Party Packages
02:02 Should I use a third-party Python Package on database data?
03:05 Performance characteristics for open-source Python packages with Embedded Python Execution
03:47 Which third-party packages can be used with OML4Py
04:27 Package Management - using pip
05:29 Package Management - using virtualenv
06:45 Embedded Python Execution - Introduction
08:05 Embedded Python Execution - Benefits
09:02 OML4Py Interfaces for Embedded Python Execution
09:26 Embedded Python Execution - functions via Python and SQL
10:58 Embedded Python Execution - functions via REST interface
11:25 Which OML4Py interface should I use for Embedded Python Execution?
12:09 Script Repository and datastore
13:11 Functions for managing scripts
13:54 OML4Py Datastore
14:45 Functions for managing datastore objects
15:26 Parallelism
16:43 Parallelism - Embedded Python Execution - Python interface
17:21 Parallelism - Embedded Python Execution - SQL interface
17:52 Interfaces for Embedded Python Execution - Python
18:30 Interfaces for Embedded Python Execution - SQL
19:08 Interfaces for Embedded Python Execution - REST
20:08 Demo
28:29 Q&A

Your Experts

    Sherry LaMonica

    Sherry LaMonica

    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 systems R and Python for data science and machine learning projects. She has worked with customers in fields as diverse as as pharmaceutical research, financial analysis, manufacturing and healthcare IT.
    Marcos Arancibia

    Marcos Arancibia   

    Marcos Arancibia is a Senior Principal Product Manager in the Oracle Autonomous Database team. He is charted with developing a comprehensive platform for enabling all customers and use cases to be successful on Autonomous Database, and working with developers to bring their Applications and workloads as well. Within Product Management he works to develop product strategy, roadmap prioritization, product positioning and product evangelization, working closely with the engineering teams in defining the product roadmap for Autonomous Database. He previously was a PM for Oracle Machine Learning, and 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   

    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, and working with internal and external customers in applying Oracle’s machine learning technologies for scalable and deployable data science projects. Mark is Oracle’s representative to the R Consortium and an Oracle adviser and founding member of the Analytics and Data Oracle User Community. He holds a bachelor's degree from Rutgers University and a master's degree from Brown University, both in Computer Science.

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