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

August 17, 2021

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.


OML Usage Highlight: ML on SailGP data: Predicting the best sailing direction
In this Office Hours for Oracle Machine Learning on Autonomous Database, Jeroen Kloosterman, Product Strategy Director for Analytics and AI in Oracle Digital in EMEA, showed how to use machine learning to solve a very common challenge in sailing: finding the best direction to sail. Using OML, he identified the optimal angle to the wind that a SailGP boat must sail to obtain the highest boat speed in the target direction, given a particular wind speed. He then visualized the results using Oracle Analytics Cloud.

SailGP is like Formula 1 on the water: the boats race over the water at speeds of up to 50 knots (60 m/h, 90 km/h). SailGP offers the perfect playground for you to learn about analytics and Machine Learning. Why? It’s a data intensive sport; each boat has over 800 IoT sensors that provide more than 12,000 data points. To deliver game-changing insights from the data, SailGP employs “Data Athletes” who analyse all this data to help the teams make the best decisions on the water and stay ahead of the game.

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.

The Oracle Machine Learning Notebooks offers an easy-to-use, interactive, multi-user, collaborative interface based on Apache Zeppelin notebook technology, and support SQL, PL/SQL, Python and Markdown interpreters. It is available on all Autonomous Database versions and Tiers, including the always-free editions.

OML includes AutoML, which provides automated machine learning algorithm features for algorithm selection, feature selection and model tuning, in addition to a specialized AutoML UI exclusive to the Autonomous Database.

OML Services is also included in Autonomous Database, where you can deploy and manage native in-database OML models as well as ONNX ML models (for classification and regression) built using third-party engines, and can also invoke cognitive text analytics.

Video Highlights
00:55 Topics for today
01:31 Announcing OML4Py is now available for Oracle Database 21c on Linux
02:28 ML on SailGP data: Predicting the best sailing direction
04:41 Video highlight: What is SailGP is and their use of the Oracle Cloud
05:42 What is SailGP?
07:58 Products used in the project
09:57 What's in the workshop?
13:08 Definition of the problem to solve
15:22 Definition of "foiling"
16:44 Wind Angle vs Boat Speed
18:42 How to find Wind Angle and Wind Speed for best Boat Speed
19:24 Machine Learning for Predicting Boat Speed
20:58 Overview of the Architecture with Oracle products
21:51 Demo description
22:30 Demo: Explore data in Oracle Analytics Cloud
26:05 Demo: Build machine learning model in Oracle Machine Learning (AutoML UI)
27:10 Request predictions directly from Oracle Analytics Cloud
29:51 Visualize the results in Oracle Analytics Cloud
35:04 Try it yourself! Workshop
35:40 Q&A

Your Experts

    Jeroen Kloosterman

    Jeroen Kloosterman

    Jeroen Kloosterman is Technology Product Strategy Director for Analytics and Data Science for Oracle's SMB market in EMEA. He has over 20 years of experience in business development, product development and sales training, and is an advocate for analytics and data science.
    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.

All Sessions