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Benefits of Data Science in the Cloud

Oracle Cloud Infrastructure Data Science brings the performance and security of Oracle Cloud, built specifically for data science teams, making data science collaborative, scalable, and powerful.

Collaborative

Collaborative

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. Data Science can transform the way teams collaborate on data-driven projects.

Scalable

Scalable

Leveraging the speed and scale of Oracle Cloud hardware, data scientists can scale up their data science workloads to tackle the big data challenges in their organizations. With no DevOps expertise, data scientists can easily build out their machine learning pipelines on Oracle Cloud, paying only for what they use.

Powerful

Powerful

Designed with the needs of the modern data scientist in mind, Oracle Cloud Infrastructure Data Science brings together the latest open source machine learning toolkit with Oracle's proprietary technology. Data scientists can use their favorite tools and libraries, while amping up their impact.

Product Features

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Collaborative Workspace

    Projects

  • Project-based user experience simplifies data science operations and enables teams to work together in the cloud.

    Access Controls

  • Integration with Oracle Cloud Identity ensures managers and admins can control access to data science assets.

    Model Catalog

  • Managed storage for model artifacts and metadata allows data scientists to track, discover, and use models created by their colleagues.

Model Lifecycle Management

    Accelerated Data Science SDK

  • A built-in Python SDK makes common data science tasks easier, faster, and less error-prone.

    Automated Machine Learning (AutoML)

  • Built into the Accelerate Data Science SDK, Oracle's proprietary AutoML offers a fast and easy way to generate accurate model candidates.

    Model Explanation

  • Oracle's proprietary Model Explanation capabilities within the Accelerated Data Science SDK enable quick and easy generation of model evaluation and interpretation metrics and visualizations.

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 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.