Skip to Main Content

Oracle Machine Learning Office Hours

Free tips and training every month! Subscribe for reminders and more from Office Hours. FAQ

Header container

October 05

15:00 UTC   Start Times Around the World

Subscribe to be notified of changes to sessions and give us feedback!

Having trouble watching the video on this page? Open the video in your browser.

Description

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

  • #SELECTION#
    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 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.
    #MISC#
    #ACTIONS#
  • #SELECTION#
    Marcos Arancibia

    Marcos Arancibia   

    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.
    #MISC#
    #ACTIONS#
  • #SELECTION#
    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, 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.
    #MISC#
    #ACTIONS#

All Sessions