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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
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
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
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
Open all Close allCollaborative Workspace
- Project-based user experience simplifies data science operations and enables teams to work together in the cloud.
Projects
- Integration with Oracle Cloud Identity ensures managers and admins can control access to data science assets.
Access Controls
- Managed storage for model artifacts and metadata allows data scientists to track, discover, and use models created by their colleagues.
Model Catalog
Model Lifecycle Management
- A built-in Python SDK makes common data science tasks easier, faster, and less error-prone.
Accelerated Data Science SDK
- Built into the Accelerate Data Science SDK, Oracle's proprietary AutoML offers a fast and easy way to generate accurate model candidates.
Automated Machine Learning (AutoML)
- 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.
Model Explanation
Open Source Support
- Built-in cloud-hosted JupyterLab notebook sessions enable teams to build and train models using Python.
Notebook Sessions
- Use popular open source visualization tools like plotly, matplotlib, and bokeh to visualize and explore data.
Visualization Tools
- Launch notebook sessions with popular machine learning frameworks like tensorFlow and scikit-learn, or bring your own packages.
Open Source Machine Learning Frameworks
Access to Data and Compute
- Leverage data stored in Oracle Object Storage Cloud or any other data source on any cloud or on premises.
Data Access
- Spin up small or large compute on Oracle Cloud Infrastructure to tackle analyses of any size.
Self-Service Scalable Compute
- Build, train, and deploy models on high-performance Oracle Cloud Infrastructure.