Machine Learning Services
Machine learning services from Oracle make it easier to build, train, deploy, and manage custom learning models. These services deliver data science capabilities with support from favorite open source libraries and tools, or through in-database machine learning and direct access to cleansed data. Supporting machine learning services provide more streamlined data labeling or improved access to virtual machines.
Discover the importance of a solid data strategy for AI. This IDC report explores current challenges and provides guidance on putting together a foundational data strategy for AI.
Building a machine learning model is an iterative process. Learn about every step from data collection to model deployment and monitoring.
Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results.
Machine learning services features
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Open source libraries and frameworks
Open source libraries and frameworks from Python and R enable data exploration, transformation, and visualization. These include, but are not limited to, pandas, Dask, NumPy, Plotly, Matplotlib, TensorFlow, Keras, and PyTorch.
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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. Data already in Oracle Database doesn’t need to be moved, reducing the data management workload.
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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 users to understand the overall behavior of a model as well as individual model predictions. OCI Data Science makes it easier to understand the importance of features and what influences predictions.
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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 and in Oracle Cloud and other clouds).
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Support for multiple scripting languages
Data scientists can develop with 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.
Why machine learning from Oracle?
Create and validate 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.
Get better results with more 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.
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 explanations helps data scientists, business analysts, and executives have confidence in the results.
Machine learning partners and customers
Oracle machine learning services
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OCI Data Science
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OCI Data Science is an end-to-end machine learning (ML) service that offers JupyterLab notebook environments and access to hundreds of popular open source tools and frameworks.
- Build and train ML models with NVIDIA GPUs, AutoML features, and automated hyperparameter tuning.
- Deploy models as HTTP endpoints or use Oracle Functions.
- Manage models through version control, repeatable jobs, and model catalogs.
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Machine Learning in Oracle Database
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Machine Learning in Oracle Database supports data exploration and preparation as well as building and deploying machine learning models using SQL, R, Python, REST, AutoML, and no-code interfaces.
- It includes more than 30 in-database algorithms that produce models in Oracle Database for immediate use in applications.
- Build models quickly by simplifying and automating key elements of the machine learning process.
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OCI Data Labeling
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OCI Data Labeling provides labeled datasets to more accurately train AI and machine learning models
- Users can assemble data, create and browse datasets, and apply labels to data records through user interfaces and public APIs.
- The labeled data sets can be exported and used for model development across many of Oracle’s AI and machine learning services for a seamless model-building experience.
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OCI Virtual Machines for Data Science
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OCI Virtual Machines for Data Science are GPU-based environments that are preconfigured with popular IDEs, notebooks, and machine learning frameworks.
- Easily deploy from Oracle Cloud Marketplace with a choice of compute shapes.
Use cases for machine learning
Prosperdtx: Improve patient outcomes with OCI Data Science
See how Prosperdtx deployed an architecture that could securely handle large amounts of source data to build predictive models with Oracle Cloud Infrastructure Data Science.
Set up a data science environment with in-database machine learning
With Machine Learning in Oracle Database, data scientists can save time by moving the data to external systems for analysis and model building, scoring, and deployment.
OCI Data Science introduces an easier way to get started with notebook sessions and jobs
Wendy Yip, Data Scientist, Oracle
Oracle Cloud Infrastructure (OCI) Data Science is introducing a new feature, managed egress, that makes it easier for customers to configure their networking for their notebooks and jobs. This feature provides the option to have your networking resources managed by OCI Data Science.
Explore notebook sessions