What is Oracle’s Data Science Strategy?

Oracle is an American MNC, based in California, that deals in database software and technology, cloud engineered systems, and enterprise software products—particularly its own brands of database management systems. The company has been estimated at a brand value of over US$ 26 billion and is ranked among the top 20 most valuable brands in the world. Oracle is also one of the top 100 largest companies in the world by market value.

The company works on the mission to help people see data in new ways, discover insights, unlock endless possibilities. It designs, manufactures, and sells both hardware and software products with its innovative solutions to sectors including financing, training, consulting, and hosting services.

Interestingly, several products that have been added to Oracle's portfolio are through acquisitions. Therefore, acquisition plays a crucial role in its brand building and peculiar technological offerings.

Oracle recently announced the availability of the Oracle Cloud Data Science Platform with Oracle Cloud Infrastructure Data Science at the core, helping enterprises to collaboratively build, train, manage, and deploy machine learning models.

The goal with Oracle's Cloud Infrastructure Data Science is to improve the collaboration and effectiveness of data science teams with capabilities such as shared projects, model catalogs, team security policies, reproducibility, and audibility. Oracle Cloud Infrastructure Data Science automatically selects the most optimal training datasets through AutoML algorithm selection and tuning, model evaluation, and model explanation.

Designed for data science teams and scientists, Oracle Cloud Infrastructure Data Science includes automated data science workflow, saving time and reducing errors with the capabilities like AutoML, Automated predictive feature selection, model evaluation, and Model explanation.

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.