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Explore the Benefits of Oracle Cloud Infrastructure Data Science Service

Build, train, deploy, and manage machine learning models, all on Oracle Cloud. Teams of data scientists can easily organize their work and access data and computing resources in a collaborative environment. The platform makes data science collaborative, scalable, and powerful—ultimately creating a more robust business, powered by machine learning.



Finally, a platform designed for data science in the enterprise. Teams of data scientists can work together in a collaborative workspace with features for granular access control and security, centralizing and organizing data science assets all in one place.



Leveraging Oracle Cloud Infrastructure, data scientists can scale up their data science workloads to tackle the biggest data challenges in their organizations with no time wasted on provisioning and configuring.



Designed for the modern data scientist, Oracle Cloud Infrastructure Data Science brings together the latest open source machine learning toolkit with Oracle's proprietary technology.

Product Features

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Accelerating Data Science Workflow


  • Automated algorithm selection and tuning automates the process of running tests against multiple algorithms and hyperparameter configurations. It checks results for accuracy and confirms that the optimal model and configuration are selected for use. This saves significant time for data scientists and, more importantly, is designed to allow every data scientist to achieve the same results as the most experienced practitioners.

    Feature Selection

  • Automated predictive feature selection simplifies feature engineering by automatically identifying key predictive features from larger datasets.

    Model Evaluation

  • Automated evaluation generates a comprehensive 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. Model evaluation goes beyond raw performance to take into account expected baseline behavior and uses a cost model so that the different impacts of false positives and false negatives can be fully incorporated.

    Model Explanation

  • Oracle Cloud Infrastructure Data Science provides automated explanation of the relative weighting and importance of the factors that go into generating a prediction. Oracle Cloud Infrastructure Data Science offers the first commercial implementation of model-agnostic explanation. With a fraud-detection model, for example, a data scientist can explain which factors are the biggest drivers of fraud so the business can modify processes or implement safeguards.

Data Science Team Support

    Shared Projects

  • Team members can organize, enable version control, and reliably share all their work, including data and notebook sessions.

    Model Catalogs

  • Team members can reliably share already-built models and the artifacts necessary to modify and deploy them.

    Security Policies

  • Team-based security policies allow users to control access to models, code, and data, which are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.

    Reproducibility and Auditability

  • Reproducibility and auditability functionality enables the enterprise to keep track of all relevant assets, so that all models can be reproduced and audited, even if team members leave.

Open Source Support

    Notebook Sessions

  • Built-in cloud-hosted JupyterLab notebook sessions enable teams to build and train models using Python.

    Visualization Tools

  • Use popular open source visualization tools like plotly, matplotlib, and bokeh to visualize and explore data.

    Open Source Machine Learning Frameworks

  • Launch notebook sessions with popular machine learning frameworks like TensorFlow, Jupyter, Dask, Keras, XGboost, and scikit-learn, or bring your own packages.

Access to Data and Compute

    Data Access

  • Leverage data stored in Oracle Object Storage Cloud or any other data source on any cloud or on premises.

    Self-Service Scalable Compute

  • Spin up small or large compute on Oracle Cloud Infrastructure to tackle analyses of any size.

    End-to-end Model Development

  • Build, train, and deploy models on high-performance Oracle Cloud Infrastructure.