R/Database: Using R at Scale on Database Data
Abstract: Many users, organizations, and enterprises rely on R as a powerful language and environment for statistical analysis, data science, and machine learning. Many of these same users work with data in – or extracted from – Oracle databases. As data volumes increase, moving data complicates the life of data professionals, like data engineers and data scientists, as well as solution developers and administrators. In this session, you’ll learn how to increase overall solution performance with R tightly integrated with Oracle databases. Oracle Machine Learning for R (OML4R) leverages the database as a high-performance computing environment, enabling users to explore, transform, and analyze data faster and at scale, while allowing the use of familiar R syntax and semantics. In-database parallelized machine learning algorithms are exposed through a natural R interface, including the use of R Formula. R users can run user-defined R functions in database-environment spawned and managed R engines using R, SQL, and REST interfaces – even taking advantage of system-enabled data parallelism. User-defined R functions and other R objects can be stored directly in the database to facilitate ease of solution deployment while benefiting from database security – avoiding the use of flat files. Join us for this engaging session highlighting multiple use cases with demonstrations.
3:33 Background: R for databases
8:43 R Packages for database access
13:22 Memory and scalability
15:24 Leveraging parallelism
22:03 OML4R introduction
26:00 Demonstration: OML4R data exploration and preparation, dplyr
31:40 In-database algorithms
34:13 Demonstration: OML4R modeling
37:14 Embedded execution
39:14 Demonstration: OML4R embedded R execution
49:01 Using additional third-party packages with ODB/ADB
50:21 Oracle Machine Learning related components
51:08 Summary and more information
Mark Hornick, Senior Director, Oracle Machine Learning
Mark Hornick is senior director of product management for Oracle Machine Learning. Mark has more than 20 years of experience integrating and leveraging machine learning with Oracle software as well as working with internal and external customers to apply Oracle’s machine learning technologies. He has been involved with R technology for the past 15 years. Mark is Oracle’s representative to the R Consortium and is an Oracle Adviser of the Analytics and Data Oracle User Community. He has been issued seven US patents. Mark holds a bachelor’s degree from Rutgers University and a master’s degree from Brown University, both in computer science. Follow him on Twitter @MarkHornick and connect on LinkedIn. He blogs at blogs.oracle.com/machinelearning.
Sherry LaMonica, Consulting MTS, Oracle Machine Learning
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 with R and Python for data science and machine learning projects. She has worked with customers in fields as diverse as pharmaceutical research, financial analysis, manufacturing, and healthcare IT.
The R Adoption Series
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