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.

Try Oracle AI and get a 30-day trial

Oracle offers a free pricing tier for most AI services as well as a free trial account with US$300 in credit to try additional cloud services. Get the details and sign up for your free account.

Explore the lifecycle of ML models

Building an ML model is an iterative process. Learn about each step, from data collection to model deployment and monitoring.

Try an ML workshop

Explore notebooks and build or test ML algorithms. Try automated ML (AutoML) and see data science results.

AI and ML features

Vector databases designed for AI

Get the benefits of AI from your data. The integrated AI Vector Search in Oracle Database and Vector Store in MySQL HeatWave add capabilities to query business and semantic data easier and faster, with more accurate results.

Open source libraries and frameworks

Use open source libraries and frameworks from Python and R for data exploration, transformation, and visualization. These include pandas, Dask, NumPy, Plotly, Matplotlib, TensorFlow, Keras, and PyTorch.

In-database optimized algorithms

Oracle Database includes more than 30 high performance algorithms offering popular ML capabilities. MySQL HeatWave AutoML supports algorithms for anomaly detection, forecasting, classification, regression, and recommender system tasks.

Easy access and deployment

Access data in multiple formats (including CSV, Excel, and JSON) from multiple sources (including object storage, Oracle Database, MySQL HeatWave, MongoDB, PostgreSQL, and Hadoop) in multiple locations. Quickly deploy models via REST API or directly in the database for access by applications and business analysts.

Query data using natural language

Interact with your SQL database using natural language prompts to help expert and nonexpert SQL users query the database. Autonomous Database Select AI lets users have a lifelike, natural language conversation with a broad range of LLMs.

Support for multiple scripting languages

Data scientists can develop applications with the most popular programming 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 use machine learning and AI for Oracle data platforms?

Create and validate models faster

Build models with an automated machine learning pipeline that includes algorithm selection, model training, feature selection, and hyperparameter optimization. Build, train, run, and explain ML models using a visual interface.

Get better results with more data

Data scientists and analysts must access data in different formats from different sources that are on-premises or in the cloud. Simplify that access by using drag-and-drop data integration and preparation tools to move data into a data lake or data warehouse.

Benefit from seamless integration

Consistent architectures and deployment methods across Oracle AI services make it simpler to work across multiple services. Built-in AI options, including vector databases, help Oracle customers leverage the power of their data.

Machine learning and generative AI for data platforms customers

Explore more AI and ML customer stories

Oracle machine learning and generative AI services

Use cases for ML and generative AI for data platforms

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

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.

GenAI with MySQL HeatWave

For a given user query, the vector store identifies the most similar documents by performing a similarity search against the stored embeddings and the embedded query.


Query and retrieve information in natural language diagram, details below

GenAI with Autonomous Database

By learning effective SQL query patterns from curated training data, LLMs can produce more efficient queries, enabling them to perform better.

January 23, 2024

The Future of Generative AI: What Enterprises Need to Know

Greg Pavlik, SVP, Oracle Cloud Infrastructure

The advent of powerful cloud infrastructures combined with advanced GPUs has enabled us to push the boundaries of technology with AI. Generative can perform an array of tasks, such as creating text and art, generating SQL queries, writing code, and assisting in product support—all of which seemed impossible just a few years ago. These feats have captured the imagination of executives who see the immense potential to harness the power of generative AI to drive business results.

Read the complete post

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