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Data science platform

A data science platform that improves productivity with unparalleled abilities. Build and evaluate higher-quality machine learning (ML) models. Increase business flexibility by putting enterprise-trusted data to work quickly and support data-driven business objectives with easier deployment of ML models.

data-science-cloud-bridge
The lifecycle of machine learning models

Building a machine learning model is an iterative process. In this ebook, we break down the process and describe how machine learning models are built.

O’Reilly: Machine learning goes to the cloud

Cloud-based machine learning can deliver business insights that create change. Find out how with this new O’Reilly ebook.

Why a data science platform from Oracle?

Create and validate high-quality models faster

Build high-quality models faster and easier. Automated machine learning capabilities rapidly examine the data and recommend the optimal data features and best algorithms. Additionally, automated machine learning tunes the model and explains the model’s results.

View the machine learning ebook (PDF)
Faster and easier data science models

Get better results by working with all data

Data scientists need to access data in different formats from different data sources, whether on-premises or in the cloud. Use drag-and-drop data integration and preparation tools to move data into a data lake or data warehouse, simplifying access for data scientists.

Read the data discovery ebook (PDF)
Data scientist access all data

Deliver trusted artificial intelligence

AI is more trusted when multiple contributors effectively collaborate, and machine learning tools provide explanation and evaluation of models. Oracle security tools and user interfaces enable multiple roles to participate in projects and share models. Model-agnostic explanation helps data scientists, business analysts, and executives have confidence in the results.

Read more about accelerated data science
Data science meet AI

Oracle data science platform

Accelerate machine learning model development

Enables data scientists to build, train, and manage machine learning models on Oracle Cloud using an open source Python ecosystem enhanced by Oracle for automated machine learning (AutoML), model evaluation, and model explanation.


Machine learning for everyone

Build and deploy machine learning models in Oracle Autonomous Database using scalable and optimized in-database algorithms.


Build machine learning models without the expense

Get up and running quickly with GPU-based environments, preconfigured with popular IDEs, notebooks, and machine learning frameworks. Easily deploy from Oracle Cloud Marketplace on your choice of compute shape.


Complete your environment with end-to-end data services

A data science platform is more than just a good set of tools for building machine learning models. Oracle's data science platform includes a complete set of capabilities to support an end-to-end data science pipeline.

Modules

AgroScout logo

AgroScout and Oracle: fighting hunger with next-gen technology

 

Explore Oracle Cloud Infrastructure
Industry: HIGH TECHNOLOGY
Location: ISRAEL
March 11, 2020

Robust AI in Financial Services

Swetasudha Panda, Senior Member of Technical Staff, Oracle Labs

Machine learning models are increasingly being used to make critical decisions in various regulated domains, such as hiring or credit/lending decisions. However on multiple occasions, such models have been reported to exhibit discriminatory behavior with respect to various legally recognized protected groups.

Featured blogs

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Data science platform features

  • AutoML

    Automated machine learning (AutoML) helps data scientists by automating algorithm selection, feature selection, and model tuning. This enables faster, more accurate results that take less compute time. AutoML also enables nonexperts to leverage powerful machine learning algorithms to build better quality models.

  • In-database optimized algorithms

    Oracle Database includes more than 30 high-performance, fully scalable algorithms covering commonly used machine learning techniques, such as anomaly detection, regression, classification, clustering, and more. Data already in Oracle Database does not need to be moved, reducing the data management workload for data scientists and allowing them to focus on building production models.

  • Open source libraries and frameworks

    Use and import open source libraries and frameworks from Python and R to enable data exploration, transformation, visualization, and machine learning. These include but are not limited to: pandas, Dask, NumPy, dplyr for transformation, Seaborn, Plotly, Matplotlib, and ggplot2 for visualization, and TensorFlow, Keras, and PyTorch for model building.

  • Choice of deployment

    Quickly deploy models for access by applications and business analysts. Models can be deployed with a REST API in a serverless, scalable cloud architecture as Oracle Functions or directly in the database.

  • Model explanation

    Model explanation enables experts and nonexperts alike to understand the overall behavior of a model as well as individual model predictions. With model explanation and prediction details, it’s easy to understand the importance of features and what most influences predictions.

  • Access any data flexibly and easily

    Access data in multiple formats (including CSV, Excel, and JSON), multiple sources (including object storage, Oracle Database, MongoDB, PostgreSQL, and Hadoop), and multiple locations (on premises, Oracle Cloud, and other clouds).

  • Support for multiple scripting languages

    Data scientists can develop data science and machine learning solutions using the most popular languages, including Python, R, and SQL. Organizations achieve better and faster results when data scientists have the flexibility to use the languages best suited to particular tasks.

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