Oracle launched a new service this week designed to make life easier for data scientists building machine learning (ML) models.

The Oracle Cloud Data Science Platform was created specifically to improve the effectiveness of data science teams, the company said, with such capabilities as shared projects, model catalogs, team security policies, reproducibility and auditability.

"Effective machine learning models are the foundation of successful data science projects," said Greg Pavlik, senior vice president of product development in Oracle's Data and AI Services group, "but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground. 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."

The new platform actually comprises seven services, including:

• Oracle Cloud Infrastructure Data Science: This is the virtual, AI-assisted workbench at the center of the platform, which gives users the ability to build, train, and manage new ML models on Oracle Cloud using Python and such open-source tools and libraries as TensorFlow, Keras, and Jupyter. The AI automates parts of the workflow.

• Oracle Autonomous Database: ML algorithms are integrated with new support for Python and automated machine learning. Upcoming integration with Oracle Cloud Infrastructure Data Science will enable data scientists to develop models using both open source and scalable in-database algorithms, the company says.

• Oracle Cloud Infrastructure Data Catalog: Allows users to discover, find, organize and trace data assets on Oracle Cloud. This service also has a built-in business glossary to support curation and discovery of trusted data.

• Oracle Big Data Service: Offers a full Cloudera Hadoop implementation, with simpler management than other Hadoop offerings, including one click to make a cluster highly available and to implement security. This service includes machine learning for Spark, which allows organizations to run Spark machine learning in memory with one product and with minimal data movement.

• Oracle Cloud SQL: Enables SQL queries on data in HDFS, Hive, Kafka, NoSQL, and Object Storage. This service is designed to enable any user, application, or analytics tool that can talk to Oracle databases to transparently work with data in other data stores, with the benefit of push-down, scale-out processing to minimize data movement.

• Oracle Cloud Infrastructure Data Flow: A fully-managed Big Data service that allows users to run Apache Spark applications with no infrastructure to deploy or manage. It's designed to allow enterprises to deliver Big Data and AI applications faster. It also provides a single window to track all Spark jobs, making it simple to identify expensive tasks or troubleshoot problems.

• Oracle Cloud Infrastructure Virtual Machines for Data Science: Pre-configured GPU-based environments with common IDEs, notebooks, and frameworks that can be up and running in under 15 minutes, the company says.

The platform was unveiled at a London event by Oracle CEO Safra Catz.