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Oracle Cloud Infrastructure (OCI) Data Science

Oracle Cloud Infrastructure Data Science helps data scientists rapidly build, train, deploy and manage machine learning models.

Get a quick overview of OCI Data Science.

Oracle Cloud Infrastructure Data Science

Open source machine learning frameworks

Dozens of popular open source tools and frameworks are included to provide familiarity and versatility for data scientists. Build machine learning models with TensorFlow, PyTorch, or add other frameworks of choice.

JupyterLab interface

Built-in, cloud-hosted JupyterLab notebook environments allows teams of data scientists to build and train models with a familiar user interface.

Data visualization tools

Popular open source visualization tools such as Plotly, Matplotlib, and Bokeh help data scientists visualize and explore data.

Data access and exploration

Oracle’s Accelerated Data Science library is a Python library that contains a comprehensive set of data connections, allowing data scientists to access and use data from many different data stores to produce better models.

Automated Machine Learning (AutoML)

The Accelerated Data Science library supports Oracle’s own AutoML, as well as open source tools such as H2O 3 and auto-sklearn. Oracle’s AutoML offers automated feature selection, adaptive sampling, and automated algorithm selection. These features, along with hyperparameter tuning ultimately generate an accurate model candidate saving the data scientist significant time.

Model evaluation

Automated evaluation generates a comprehensive suite of evaluation metrics and visualizations to measure model performance against new data and compare model candidates to make it easier for the data scientist to produce a high-quality model.

Model explanation

Accelerated Data Science model explanation includes global and local explanations to help explain the overall behavior of a model, as well as specific model predictions. For model consumers, automated model-agnostic explanations improve understanding and trust, address regulatory needs, and increase the speed of machine learning adoption.

Oracle Functions

Easily deploy data science models as Oracle Functions—a highly-scalable, on-demand and serverless architecture on Oracle Cloud Infrastructure that simplifies deployment for data scientists and infrastructure administrators.

Model catalogs

Team members use the model catalog to preserve and share completed machine learning models and the artifacts necessary to reproduce, test, and deploy them.

Reproducibility and auditability

Conda Environments and model catalog features allow organizations to reproduce the original model code, library, and training dataset dependencies. This allows data scientists to re-train, reproduce, and audit machine learning models.

Data science team collaboration

Shared projects

Team members use projects to organize, enable version control, and reliably share all their work, including data and notebook sessions.

Security policies

Team-based security policies allow users to include team members in projects. These policies control access to models, code, and data to facilitate collaboration but also to protect work. Security controls are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.

Version control

Users connect to their organization’s Git repository to preserve and retrieve machine learning work.

Flexible data access

Any data source in any cloud or on-premises can be accessed and used by data scientists for building machine learning models providing more potential data features that lead to better models.

Self-service, on-demand compute and storage

Users select the amount of compute and storage resources they need to tackle projects of any size without worrying about provisioning or maintaining infrastructure.

Powerful hardware, including graphics processing units (GPUs)

Data scientists can build and train deep learning models in much less time using GPUs in notebook sessions. Oracle Cloud Infrastructure Data Science offers support for NVIDIA P100 and V100 GPUs.

Victoria University logo

Victoria University Accelerates Research with Oracle Cloud Infrastructure Data Science

Victoria University researchers turned to Oracle Cloud to try to predict domestic violence incidents reported on social media.

Key benefits

  • Open source tools provide familiarity and productivity for data scientists

    Use Python, the most popular language for data science, with JupyterLab and more than 300 open source libraries and frameworks including Dask, scikit-learn, and XGBoost. Or, customize the environment for ultimate flexibility.

  • Oracle’s toolkit accelerates model building

    Accelerate model building with automation from Oracle Accelerated Data Science library, making it easier to prepare data, and select then tune the best algorithm with AutoML, all resulting in higher-quality models

    Faster machine learning

  • Simplified infrastructure improves user productivity and reduces management costs

    Empower users to choose and change the amount of compute and storage needed for the notebook development environments —provisioning is automated.

    Model explanation improves trust in results

    Improve trust and understanding of models by showing how data influences model results. Experts and nonexperts alike use model explanation to understand and validate what caused a model to return a particular result and identify hidden bias.

    Watch this video on Accelerated Data Science

    Rapidly deploy scalable models

    Quickly deploy models in the cloud on a fully managed platform that automatically scales in response to demand.


Data Science Notebook Sessions

Unit Price
Compute - Virtual Machine Standard - E2

vCPU per hour
Compute - Virtual Machine Standard - X7

vCPU per hour

GPU per hour
VM.GPU3.x (NVIDIA V100 Tensor Core - 16 GB)

GPU per hour
Block Volume Storage

Gigabyte storage capacity per month
Block Volume Performance Units

Performance Units per Gigabyte per month (10 VPUs at $0.017 for Balanced Performance)

Data Science Models

Unit Price
Object Storage - Storage

Gigabyte storage capacity per month

Common cloud industry practice is to define compute instances based on the number of virtual CPUs (vCPUs) they include. Each vCPU provides the capacity for one thread of execution. A vCPU does not provide a whole physical compute core, it’s part of a core. In contrast, Oracle’s x86 compute shapes use OCPUs which equate to physical CPU cores, each of which provides for two threads. To make it easier for customers to compare across cloud service providers, Oracle presents vCPU pricing on our web pages while billing is based on the number of OCPU time they consume. The per-hour OCPU rate customers are billed at is twice the vCPU price on the web pages since they receive two vCPUs of compute power instead of one.



Get to know the data science cloud service

An interactive tour of the user interface.

Cloud learning

Discover more about data science

Learn about using Oracle Cloud Infrastructure Data Science.

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