Machine Learning Services

Access a full range of machine learning (ML) and generative AI innovations, including vector databases, fully integrated in Oracle’s data platforms. Work with in-database tools and algorithms to build, manage, and deploy ML models and get more accurate, contextually relevant answers from generative AI by combining large language models (LLMs) with your proprietary data.

Accelerate innovation and competitive advantage with AI

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.

The lifecycle of machine learning models

Building a machine learning model is an iterative process. Learn about every step from data collection to model deployment and monitoring.

Try a machine learning workshop

Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results.

Machine learning services features

  • 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.

  • In-database optimized algorithms

    Oracle Database includes more than 30 high performance, fully scalable algorithms covering commonly used ML techniques. MySQL HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks. With in-database ML, there’s no need to move the data to a separate machine learning service.

  • 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 users to understand the overall behavior of a model as well as individual model predictions, helping organizations with regulatory compliance, fairness, repeatability, causality, and trust. MySQL HeatWave and Oracle Cloud Infrastructure (OCI) Data Science make it easier to understand the importance of features and what 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, MySQL HeatWave, MongoDB, PostgreSQL, and Hadoop), and multiple locations (on-premises, in Oracle Cloud and in other clouds).

  • 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.

Machine learning partners and customers

Explore more ML customer stories

Oracle machine learning services

OCI Data Science

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.

Machine Learning in Oracle Database

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.

Machine Learning in MySQL HeatWave

HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.

  • Easily and securely apply machine learning training, inference, and explanation to data stored both inside MySQL and in the object store.
  • HeatWave AutoML automates the machine learning lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization.
  • By considering both implicit feedback (past purchases, browsing behavior, etc.) and explicit feedback (ratings, likes, etc.), the HeatWave AutoML recommender system generates personalized recommendations.

OCI Data Labeling

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.

OCI Virtual Machines for Data Science

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.

Prosperdtx architecture diagram, details below
Data from electronic health records, devices, and end users is collected to build predictive models to use in healthcare applications. Data streamed from wearable devices and from imaging records is collected in OCI Object Storage. Structured data is securely loaded and stored in Oracle Autonomous Database. Oracle APEX helps developers quickly build applications. OCI Data Science is used to build predictive models capable of consuming large amounts of patient data. Application developers take the finished predictive models and add them to applications.

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.

Machine learning architecture diagram, details below
Data is generated from a customer data center and sent to Oracle Autonomous Database for storage. Oracle Autonomous Database has Machine Learning in Oracle Database embedded inside, which means data scientists can build models quickly by simplifying and automating key elements of the ML lifecycle. Completed models are sent to Oracle Analytics Cloud or Oracle APEX. Business analysts embed completed models in analytics projects, while application developers embed them in applications.

Easily deliver ML-powered recommendations with MySQL HeatWave

MovieHub application
Machine learning-powered recommendations with MySQL HeatWave

The sample MovieHub application showcases how the MySQL HeatWave AutoML recommender system generates personalized, machine learning–powered recommendations. Follow our step-by-step instructions to build the MovieHub app using Oracle APEX—no coding required.

March 11, 2022

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

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