Oracle recently announced the release and availability of the Oracle Cloud Data Science Platform, according to a press release on the company’s website. The product is built atop the Oracle Cloud Infrastructure Data Science, which enables organizations to build, train, manage and deploy machine learning models. Oracle Cloud Infrastructure Data Science includes capabilities like shared projects, model catalogs, team security, reproductibility and auditability. It automatically selects optimal training datasets through AutoML algorithm selection and tuning, model evaluation and model explanation.
Oracle Cloud Infrastructure Data Science includes automated data science workflows and AutoML selection and tuning that automates the process of running tests against multiple algorithms and hyperparameter configurations. Automated predictive feature selection simplifies feature engineering by identifying key predictive features from larger datasets.A model evaluation capability generates a suite of evaluation metrics and suitable visualizations to measure model performance against new data and can rank models over time to enable optimal behavior in production. There’s also model explanation of the relative weighting and importance of factors that go into prediction.
The platform features seven new services in total, designed to improve data science results. Machine learning algorithms are integrated into the Oracle Autonomous Database with support for Python and automated machine learning. The Oracle Cloud Infrastructure Data Catalog allows users to discover, organize, enrich and trace data on the vendor’s cloud, while Oracle Big Data Service includes a full Cloudera Hadoop implementation. Oracle Cloud SQL enables SQL queries on data in HDFS, Hive, Kafka, NoSQL and object storage.
Oracle Cloud Infrastructure Data Flow is a fully-managed big data service that enables users to run Apache Spark applications with no infrastructure to deploy or manage. Oracle Cloud Infrastructure Virtual Machines for Data Science is a pre-configured GPU-based environment with common IDEs, notebooks and frameworks. Then there’s Oracle Cloud Infrastructure Data Science, which allows users to build, train and manage machine learning models on Oracle Cloud using Python and other open-source tools and libraries.
In a media statement about the release, Oracle Senior Vice President of Product Development at Oracle Data and AI Services Greg Pavlik said: “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. 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.”
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