Just in case you haven’t heard, London recently played host to one of the cloud industry's biggest events of the year: Oracle OpenWorld Europe, marking one of many major stops in Oracle OpenWorld’s global tour (seriously—they stopped through Dubai in January, and are now on their way to Singapore and Sao Paulo).
Oracle OpenWorld Europe brought together a range of keynote speakers and sessions, featuring thought leaders, entrepreneurs, and innovators of the future from around the world. Taking place over two jam-packed days, this year’s OpenWorld Europe aimed to enable visitors to immerse themselves in the infinite possibilities of a data-driven world.
If you weren’t one of the thousands of attendees to head to London from February 12th-13th to catch the event, we’ve got you covered. Here’s the single biggest announcement to emerge from Oracle OpenWorld Europe—and what it means for the future of cloud computing in 2020 and beyond.
Oracle Announces Its Oracle Cloud Data Science Platform
For many, data science is a big black box. Some might picture a scientist sitting at a computer, crunching numbers and analyzing patterns until an “a-ha” moment is revealed.
Spoiler: That’s not exactly how it works. For good data science to happen, it’s a huge team effort. Someone has to find and prepare datasets—which can include literally any piece of information, such as a location, a name, a person’s age, or a social media comment. Then, another person has to introduce the data into a computer, using open source tools to apply statistical techniques to suggest potential relationships—all in an attempt to hopefully arrive at some grand finale of a conclusion.
And finally, when the process yields a valuable insight or conclusion, someone is responsible for publishing the model as a proven, repeatable process to run on future datasets.
Unfortunately, it doesn’t always happen that way.
In reality, “most organizations are seeing only a fraction of the enormous potential of their data,” said Greg Pavlik, Oracle’s senior vice president of product development for data and AI services, said in press release.
That’s because when there’s so many people and process involved, data can get lost, information isn’t shared, or algorithms don’t run properly.
During Oracle OpenWorld Europe, however, Safra Catz, the CEO for Oracle, made an announcement that’s aiming to change all that: The introduction of the Oracle Cloud Data Science Platform. At the core of this platform is Oracle Cloud Infrastructure Data Science, which is designed to help enterprises work more collaboratively than ever before. It provides users with the ability to build, train, and manage machine learning algorithms on Oracle Cloud using other popular data science tools like Python, TensorFlow, Keras and Jupyter. The ultimate goal of this new platform is to improve the effectiveness of data science teams with capabilities like shared projects, model catalogs, team security policies, and reproducibility features.
And because Oracle Cloud Infrastructure Data Science is built on Oracle’s powerful cloud infrastructure, “we make it easy for you to get access to not just the languages and libraries and tools, but also the computer resources that are required,” Pavlik added. This includes integrated cloud services for big data management and access to an array of open source data stores and virtual machines for data science.
With this announcement, Oracle is aiming to take on platforms from competitors such as Alteryx, KNIME Analytics Platform, and RapidMiner with a focus on automating the data science workflow.
“Effective machine learning models are the foundation of successful data science projects, but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground,” said Pavlik. “With Oracle Cloud Infrastructure Data Science, we’re improving the productivity of individual data scientists by automating their entire workflow and adding strong team support for collaboration to help ensure that data science projects deliver real value to businesses.”
Oracle Announces Six Additional Services
Oracle Cloud Infrastructure Data Science sits at the core of the new Oracle Cloud Data Science Platform, but Oracle also unveiled six other data and machine learning services to support the platform:
- New machine learning capabilities in Oracle Autonomous Database: Oracle has added support for Python and automated machine learning to Oracle Autonomous Database.
- Oracle Cloud Infrastructure Data Catalog: The data catalog provides the ability to discover, find, organize, enrich and trace data assets.
- Oracle Big Data Service: This service offers a full Cloudera Hadoop implementation, as well as machine learning for Spark.
- Oracle Cloud SQL: This service gives users the ability to run SQL queries on data in HDFS, Hive, Kafka, NoSQL, and Object Storage.
- Oracle Cloud Infrastructure Data Flow: This fully managed service lets users run Apache Spark applications without deploying or managing infrastructure.
- Oracle Cloud Infrastructure Virtual Machines for Data Science: This service offers preconfigured GPU-based environments for $30 a day.
For more information on Oracle Cloud Infrastructure, visit https://www.velocitycloud.com/platforms/oci!