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Актуальные вопросы
Data science platform
Oracle's data science platform 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.
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.
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)
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)
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
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 and Oracle: fighting hunger with next-gen technology
Robust AI in Financial Services
Swetasudha Panda, Senior Member of Technical Staff, Oracle LabsMachine 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.
Data science platform features
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AutoML
Automated machine learning (AutoML) helps all data scientists by automating algorithm selection, data and feature selection, and model tuning. This enables faster time to results, more accurate and reliable results, and less compute time.
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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.
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Open source libraries and frameworks
Use and import open source libraries and frameworks of choice to enable data transformation, visualization, and model building. These include, but are not limited to: pandas, Dask, and NumPy for transformation, Seaborn, Plotly, and Matplotlib for visualization, and TensorFlow, Keras, and PyTorch for model building.
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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.
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Model explanation
Model explanation enables experts and nonexperts alike to understand what caused a model to return a particular result. With model explanation, it’s easy to understand the importance of features, and how to generate more, or less, of an outcome.
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Access any data flexibly and easily
Use Python to access data in many different formats (including CSV, Excel, JSON, and more), many different sources (including object storage, Oracle Database, MongoDB, PostgreSQL, and more), and many different locations (on-premises, Oracle Cloud, and other clouds).
Resources
Free hands-on product experience
Try out tools for building machine learning models. No need to sign up for a cloud account.
Get started
See the official Oracle data science Twitter feed.
Subscribe to our blog
Get the latest news and tips from Oracle data science.
Contact sales
Contact Oracle’s global sales team to learn more about Oracle data science and machine learning.