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Oracle Machine Learning Office Hours

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June 15

15:00 UTC   Start Times Around the World

Description

OML usage highlight: Machine Learning Recommendations for Maintenance and Repair
This week in our Office Hours for Oracle Machine Learning on Autonomous Database Lee Sacco, Senior Director Depot Repair Development presented the current integrations and usage of Machine Learning from OML in the EBS Depot Repair Application.

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:
01:38 Why NOT start with the Data in Machine Learning
03:55 Starting with the Business Understanding
05:08 What is Oracle EBS Depot Repair?
08:38 User Schema for Repairs
12:18 Structured Data of EBS Depot Repairs
16:40 EBS Depot Repair - Technician Portal
18:42 Oracle Machine Learning recommendations for the Technician
20:53 Automatic Updates to the Technician Portal
21:38 Architecture for ML recommendations - what happens in the back-end?
26:27 Q&A
38:50 Link to a Live Demo

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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.
Lee Sacco
Lee Sacco, Senior Director, Applications Development
Senior Director of Applications Development for Oracle Maintenance Cloud and EBS Depot Repair. 15+ years of experience designing and building enterprise software for maintenance, logistics, CRM, banking and online payments. Currently focused on reliability engineering, failure analytics, and predicting when machines are going to break. Prior to Oracle worked 5 years as a technology integrator for Andersen Consulting. Holds a Bachelor of Science in Symbolic Systems from Stanford University and an MBA from the Swiss Federal Institute of Technology.

All Sessions

August 11 2021 16:00:00 UTCOracle Machine Learning Office Hours
June 29 2021 15:00:00 UTCHands-on Lab: Machine Learning 101 Classification
June 22 2021 15:00:00 UTCML 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
February 23 2021Hands-On Lab using Oracle Machine Learning for Python on Autonomous Database
February 18 2021Machine Learning 102 - Feature Extraction
December 17 2020Machine Learning 101: Feature Extraction
November 5 2020Machine Learning 102: Clustering
October 28 2020Oracle Machine Learning for R: An Introduction
September 29 2020Machine Learning 101: Clustering
August 4 2020Machine Learning 102: Regression