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Oracle Data Science Cloud

Rapidly build, train, deploy, and manage machine learning models with a data science cloud platform built for teams.

OCI Data Science

Data access and data wrangling

Flexible data access

Data scientists can access and use any data source in any cloud or on-premises. This provides more potential data features that lead to better models.

Data preparation at scale with OCI Data Flow

Integration with OCI Data Flow provides an easy interface to create and run large-scale Spark jobs from the Data Science environment. In addition, a PySpark environment enables iterative development of Data Flow applications in notebook sessions.

Automated data profiling and preparation

Streamline exploratory data analysis workflows with cutting-edge data profiling capabilities, smart visualizations, and data preparation tools.

Open source data transformation and visualization tools

OCI Data Science supports the data scientist’s favorite open source data manipulation tools such as Pandas, Dask, and Numpy, as well as popular open source visualization tools such as Plotly, Matplotlib, and Bokeh to help data scientists explore data.

Model building

JupyterLab interface

Built-in, cloud-hosted JupyterLab notebook environments allow data science teams to build and train models using a familiar user interface.

Open source machine learning frameworks

OCI Data Science provides familiarity and versatility for data scientists, with hundreds of popular open source tools and frameworks. Build machine learning models with TensorFlow or PyTorch, or add frameworks of choice.

Model training

Powerful hardware, including graphics processing units (GPUs)

With NVIDIA GPUs, data scientists can build and train deep learning models in less time. When compared to CPUs, performance speedups can be 5 to 10 times faster.

Automated machine learning (Oracle AutoML)

The Accelerated Data Science library supports Oracle AutoML, as well as open source tools such as H2O 3 and auto-sklearn. AutoML offers adaptive sampling, automated feature selection, algorithm selection, and hyperparameter tuning. AutoML generates an accurate model candidate to save the data scientist significant time.

Automated hyperparameter tuning

Save time and effort by tuning models with automated hyperparameter tuning using the ADS Tuner feature.

Model evaluation and explanation

Model evaluation

Automated evaluation generates a comprehensive suite of metrics and visualizations to measure model performance against new data and to compare model candidates. This makes it easier for data scientists to produce high-quality models.

Model explanation

Automated model explanation includes global and local explanations with specific model predictions for the overall behavior of a model. For model consumers, automated model-agnostic explanations improve understanding and trust, address regulatory needs, and increase the speed of machine learning adoption.

Model deployment

Managed model deployment

Deploy machine learning models as HTTP endpoints for serving model predictions on new data in real time. Simply click to deploy from the model catalog, and OCI Data Science handles all infrastructure operations, including compute provisioning and load balancing.

Model deployment on Oracle Functions

Easily deploy data science models as Oracle Functions—a highly scalable, on-demand, serverless architecture in Oracle Cloud Infrastructure.

Model management

Model catalog

Team members use the model catalog to preserve and share completed machine learning models. The catalog stores the artifacts and captures metadata around the taxonomy and context of the model, hyperparameters, definitions of the model input and output data schemas, and detailed provenance information about the model origin including the source code and the training environment where the model was trained.

Reproducible environments

Leverage prebuilt, curated conda environments to address a variety of use cases and like NLP, graph analytics, Spark, and NVIDIA RAPIDS. Publish custom environments and share with colleagues, ensuring reproducibility of training and inference environments.

Reusable jobs

Data scientists can move from experimentation to production by operationalizing data science tasks with jobs. Jobs are used to automate repeatable tasks such as retraining and redeploying models.

Version control

Data scientists can connect to their organization’s Git repository to preserve and retrieve machine learning work.

View All Customer Successes

OCI Data Science customer successes

Customers use OCI Data Science to improve data science collaboration and to save time and costs building machine learning models.

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prosperdtix

Prosperdtx personalizes healthcare plans using Oracle Cloud

Data Science key benefits

  • Open source tools provide familiarity and productivity for data scientists

    Use Python, the most popular language for data science, with JupyterLab and hundreds of open source libraries and frameworks like Dask, scikit-learn, and XGBoost. Or, install libraries of choice for ultimate flexibility.

  • Oracle’s Accelerated Data Science library toolkit accelerates the entire data science workflow

    Accelerated Data Science (ADS) is an end-to-end Python library covering the entire data science lifecycle, making it faster and easier to produce high-quality models.

    Faster machine learning

    Watch this video on Accelerated Data Science (1:01)

  • Fully managed infrastructure improves productivity and reduces management costs

    With self-service, on-demand infrastructure, data scientists select the amount of compute and storage resources they need to tackle projects of any size, without worrying about provisioning or maintaining infrastructure.

    Secure for the enterprise

    Team-based security policies allow data scientists to include team members in projects. These policies control access to models, code, and data, facilitating collaboration while protecting sensitive work. Security controls are fully integrated with Oracle Cloud Infrastructure Identity and Access Management.

    Tutorial and example notebooks provide expertise and best practices

    Access dozens of tutorial and example notebooks, covering topics from how to access data to the math behind model explanation techniques. Get a jumpstart on tackling different business problems with proven methodology and implementation tips.

    Try data science

Data Science pricing

Data Science Notebook Sessions

Product
Comparison Price ( /vCPU)*
Unit Price
Unit
Compute - Standard - E2


OCPU per hour
Compute - Virtual Machine Standard - X7


OCPU per hour
Compute - Standard - E3 - OCPU


OCPU per hour
Compute - Standard - E3 - Memory


Gigabyte per hour
VM.GPU2.1 (NVIDIA P100)


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 GB/month

Data Science Model Catalog Storage

Product
Comparison Price ( /vCPU)*
Unit Price
Unit
Object Storage - Storage


Gigabyte storage capacity per month

Data Science Model Deployment

Product
Comparison Price ( /vCPU)*
Unit Price
Unit
Compute - Virtual Machine Standard - X7


OCPU per hour
Load Balancer Base


Load Balancer hour
Load Balancer Bandwidth


Mbps per hour
Block Volume Storage


Gigabyte storage capacity per month
Block Volume Performance Units


Performance units per GB / month (10 VPUs at $0.017 for balanced performance)

Data Science Jobs

Product
Comparison Price ( /vCPU)*
Unit Price
Unit
Compute - Virtual Machine Standard - X7


OCPU per hour
VM.GPU2.1 (NVIDIA P100)
GPU per hour
VM.GPU3.x (NVIDIA V100 Tensor Core – 16 GB)


GPU per hour
Block Volume Storage


GB storage capacity/month
Block Volume Performance Units


Performance units per GB / month (10 VPUs at $0.017 for balanced performance)

*To make it easier to compare pricing across cloud service providers, Oracle web pages show both vCPU (virtual CPUs) prices and OCPU (Oracle CPU) prices for products with compute-based pricing. The products themselves, provisioning in the portal, billing, etc. continue to use OCPU (Oracle CPU) units. OCPUs represent physical CPU cores. Most CPU architectures, including x86, execute two threads per physical core, so 1 OCPU is the equivalent of 2 vCPUs for x86-based compute. The per-hour OCPU rate customers are billed at is therefore twice the vCPU price since they receive two vCPUs of compute power for each OCPU, unless it’s a sub-core instance such as preemptible instances. Additional details supporting the difference between OCPU vs. vCPU can be accessed here.

March 19, 2021

Model deployment for real-time predictions is now available in Oracle Cloud Infrastructure Data Science

Tzvi Keisar, Senior Principal Product Manager, Oracle

Oracle Cloud Infrastructure (OCI) Data Science released a new feature called Model Deployment to enable the serving of machine learning models as HTTP endpoints and provide real-time scoring of data.

Read the complete post

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Get to know OCI Data Science cloud service

An interactive tour of the user interface.

Cloud learning

Discover more about data science

Learn about OCI Data Science.

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Get started with data science


Product tour

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Hands-on lab

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OCI Data Science Github Repository

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