Machine learning (ML) services from Oracle make it easier to build, train, deploy, manage, and explain custom learning models. These services deliver data science capabilities with support from preferred 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.
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.
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.
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 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 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).
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.
Quickly and easily build high-quality models with an automated machine learning pipeline that includes algorithm selection, model training, feature selection, and hyperparameter optimization. Business analysts can build, train, run, and explain ML models using a visual interface—without any coding or SQL commands. Predictions are delivered with an explanation of the results.
Data scientists and analysts 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 and analysts.
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.
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.
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.
HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.
OCI Data Labeling provides labeled datasets to more accurately train AI and machine learning models
OCI Virtual Machines for Data Science are GPU-based environments that are preconfigured with popular IDEs, notebooks, and machine learning frameworks.
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.
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.
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