Oracle recently announced the availability of its Cloud Data Science Platform, a native service on Oracle Cloud Infrastructure (OCI), designed to let teams of data scientists collaborate on the development, deployment and maintenance of machine learning models.
The Cloud Data Science Platform's origin lies in the acquisition of DataScience.com that Oracle acquired in 2018 and which centralizes data science tools, projects, and infrastructure in an entirely governed workspace. Furthermore, it marks Oracle's effort to integrate artificial intelligence (AI) and ML into its cloud infrastructure.
At the core of the Cloud Data Science Platform lies the Oracle Cloud Infrastructure Data Science service, providing data scientists with the ability to build, train, and manage machine learning algorithms on the Oracle Cloud using Python, TensorFlow, Keras, Jupyter and other popular data science tools. Furthermore, it offers capabilities such as shared projects, model catalogs, team security policies, reproducibility and auditability.
Greg Pavlik, senior vice president product development, Oracle Data and AI Services, stated in the announcement of the Cloud Data Science Platform:
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.
In addition to the Oracle Cloud Infrastructure Data Science service, Oracle added six other services to the platform, including new machine learning capabilities integrated in Oracle Autonomous Database, Oracle Cloud Infrastructure Data Catalog, Oracle Big Data Service, Oracle Cloud SQL, Oracle Cloud Infrastructure Data Flow, and Oracle Cloud Infrastructure Virtual Machines for Data Science.
With the Cloud Data Science Platform, Oracle enters a market in which other Cloud Providers like AWS, Azure, GCP and IBM Cloud have already provided an ML Platform as a Service (PaaS) offering since 2016. However, as a latecomer to the market, Oracle is aiming to harness the "second mover" advantage and leapfrog the services and existing related developer workflows that other public cloud vendors offer.
Holger Mueller, principal analyst and vice president at Constellation Research Inc., told InfoQ:
Data Scientists fuel next-gen application loads that take advantage of AI and ML. Those workloads are best to run in the public cloud, as the public cloud provides elastic and infinite compute capabilities. And with that, all cloud infrastructure vendors want to attract that workload by making it easy for data scientists to build AI / ML apps, which is good news for enterprises as the speed of development is critical for next-generation applications. CxOs need to take an eye on potential lock-in from tooling, AI runtime and the data side.