Artificial Intelligence (AI)
Oracle AI is a family of artificial intelligence and machine learning services. Developers can add prebuilt models to applications and operations. Data scientists can build, train, and deploy models with favorite open source frameworks or choose to benefit from the speed of in-database machine learning.
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Building a machine learning model is an iterative process. Learn about every step, from data collection to model deployment and monitoring.
Discover how to develop a strategic investment model for scaling AI. This Gartner report outlines recommendations for the right AI investment mix and provides a formula for calculating ROI.
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Oracle AI
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Artificial intelligence services
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Oracle's AI services provide pretrained models that can be custom trained with an organization’s own data to improve model quality, making it easier for developers to adopt and use AI technology.
- Oracle Digital Assistant is an AI service that offers prebuilt skills and templates to create conversational experiences for business applications and customers through text, chat, and voice interfaces.
- Oracle Cloud Infrastructure Language makes it possible to perform sophisticated text analysis at scale. With pretrained models built in, developers don’t need machine learning expertise to build sentiment analysis, key phrase extraction, text classification, named entity recognitions, and more into their applications.
- Oracle Cloud Infrastructure Speech uses automatic speech recognition (ASR) to convert speech to text. Built on the same AI models used for Oracle Digital Assistant, developers can use Oracle’s time-tested acoustic and language models to provide highly accurate transcription for audio or video files across many languages.
- Oracle Cloud Infrastructure Vision applies computer vision to analyze image-based content. Developers can easily integrate pretrained models into their applications with APIs or custom train models to meet their specific use cases. These models can be used to detect visual anomalies in manufacturing, extract text from documents to automate business workflows, and tag items in images to count products or shipments.
- Oracle Cloud Infrastructure Anomaly Detection enables developers to more easily build business-specific anomaly detection models that flag critical incidents, resulting in faster time to detection and resolution. Specialized APIs and automated model selection simplify training and deploying anomaly detection models to applications and operations.
- Oracle Cloud Infrastructure Forecasting delivers time series forecasts through machine learning and statistical algorithms without the need for data science expertise. Developers can easily implement the service through APIs to predict a variety of metrics including product demand, revenue, and resource requirements, all with confidence intervals and explainability to aid business decision-making.
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Machine learning services
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Oracle's machine learning services help data scientists collaboratively build, manage, and deploy machine learning models with favorite open source frameworks or benefit from the speed of in-database machine learning.
- Oracle Cloud Infrastructure Data Science makes it possible to build, train, and manage machine learning models on Oracle Cloud using open source Python, with added capabilities for automated machine learning (AutoML), model evaluation, and model explanation.
- 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.
- Oracle Cloud Infrastructure 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 datasets can be exported and used for model development across many of Oracle’s AI and machine learning services for a seamless model-building experience.
- Oracle Cloud Infrastructure 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.
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AI apps for SaaS services
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Oracle AI Apps help you work more efficiently and effectively through preintegrated, fully functional AI capabilities that surface outputs directly inside the software that supports your key business functions.
- Oracle AI Apps deliver intelligent features across our Fusion Cloud applications, including CX, ERP, HCM, and SCM, to help you accelerate business processes, improve customer experiences, and manage suppliers.
- Oracle AI Apps for Finance help you optimize working capital and increase automation across payables and receivables in Oracle Fusion Cloud Financials.
- Oracle AI Apps for Human Resources enables you to improve the candidate experience and optimize your talent acquisition and talent management processes.
- Oracle AI Apps for Sales help you manage leads and opportunities more effectively, accelerate deals, and grow your business.
- Oracle AI Apps for Service delivers intelligent features directly to your service agents, augmenting their skills and enabling you to provide a superior customer experience.
- Oracle AI Apps for Procurement embeds AI-curated data directly into your procurement system, helping you validate supplier profiles, prevent fraudulent transactions, and manage supplier risk.
Artificial intelligence customer successes
Use cases for AI
- Provide chatbot support
- Analyze customer feedback
- Extract data from images
- Minimize disruptions
- Build predictive models
- Use database ML
Anomaly detection for managing assets and predictive maintenance
Use OCI Anomaly Detection to ensure optimal operation of assets while avoiding excess cost and minimizing operational disruptions in smart manufacturing.
Data sources: data is collected from one or more sources. Data collection: data is stored in oci object storage. OCI Anomaly Detection: builds model during the training phase and runs the anomaly detection algorithms during the production phase. Results: The results of the anomaly detection process are sent to one or more apps that consume the data and prepare it for presentation to end users.
Handle user tasks with Oracle Digital Assistant
Improve customer satisfaction and reduce the cost of providing chat-based support with Oracle Digital Assistant’s skill bots.
Data sources: The user accesses Oracle Digital Assistant from a messaging platform via a channel. Data collection: Oracle Digital Assistant determines through dialog what the user wants to do and gathers information to complete the task. OCI Anomaly Detection: If the user needs assistants, the digital assistant transfers the chat session to Oracle RightNow Chat Cloud Service, which then routes the user request to the correct queue based on chat rules. Results: An agent who is monitoring the queue from the agent console accepts the request.
Prosperdtx: Improve patient outcomes with OCI Data Science
See how Prosper Digital Therapeutics deployed an architecture that could securely handle large amounts of source data to build predictive models with OCI Data Science.
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.
Use OCI Language with customer feedback analytics
Use OCI Language to automate text analysis at scale and understand unstructured text in documents, customer feedback interactions, or support tickets to improve customer experience and increase efficiency.
Customer feedback reviews are gathered from applications. The feedback is stored in a CSV file in OCI Object Storage. OCI Data Integration reads the data from the data source. For each customer review, OCI Data Integration calls OCI Language. OCI Language extracts a list of aspects and their related sentiments from each record. It also extracts the list of entities mentioned in the text. OCI Data Integration writes the results to Oracle Autonomous Data Warehouse. The results are stored in ADW for analysis. Oracle Analytics Cloud visualizes the extracted insights, creates charts from the extracted tables, and filters the data.
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.
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.
Use OCI Vision to extract data from images and scanned documents
Extract data from images and scanned documents, and then use OCI Vision’s pretrained models to conduct optical character recognition, image classification and object detection, document classification, anomaly detection, and more.
Photographs or document images are uploaded to OCI Object Storage. OCI Events Service detects the new file and triggers OCI Functions. OCI Functions calls the OCI Vision API to process the images. OCI Vision retrieves each file from OCI Object Storage, analyzes the images, and stores results in OCI Object Storage. Applications retrieve the results and apply them in use cases across manufacturing, retail, health services, and other industries.
Artificial intelligence pricing
Oracle AI offers a free or low-cost pricing tier, making it affordable for everyone to experiment with building machine learning models or adding prebuilt AI models to applications.
Add prebuilt AI models to apps for US$0 cost (up to 1,000 or more transactions)
OCI Anomaly Detection
OCI Language
US$0/transaction
for the first 1,000 transactions or more, depending on the service
Build, train, manage, and deploy ML models, with budget for experimentation
No additional service charges for OCI Data Science and Machine Learning in Oracle Database
Only pay for compute and storage
Reduce your costs by matching compute to workload profile with preemptible and burstable instances
VM.STANDARD.E3.FLEX
1 OCPU 1 GB RAM
US$0.007/hour
Deploy low-latency, hot-deployed models into production without breaking the budget
VM.Standard2.1
1 load balancer base
10 mb/sec
US$0.076/hour
for a model serving real-time requests 24/7 (US$54/month)
Get started with Oracle Artificial Intelligence
Try Oracle AI and get a 30-day trial
Oracle offers a free pricing tier for most AI services as well as US$300 in free credits with a trial account to try additional cloud services. Get the details and sign up for your free account.
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Which Oracle AI and ML services offer a free pricing tier?
- OCI Anomaly Detection
- OCI Language
- OCI Data Labeling
- Machine Learning in Oracle Database
And only pay compute and storage charges for OCI Data Science.
Learn with an AI hands-on lab
The best way to learn is to try it yourself. Use our tutorials and hands-on labs with your own Oracle Cloud tenancy, with no charge for many services.
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Speed up data science with the Accelerated Data Science SDK
Explore concepts in OCI Data Science to improve workflow efficiency and become more productive.
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Introduction to OCI Anomaly Detection
In just a few steps, build a machine learning model to detect anomalies in production with OCI Anomaly Detection.
Start this lab Introduction to OCI Language
Explore OCI Language and perform NLP tasks such as detecting languages, extracting key elements, classifying intent, and detecting sentiment in the text with a few simple steps.
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Build the perfect digital assistant for your business
Explore how to work with multiple chatbots and digital assistants in Oracle Digital Assistant.
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Artificial intelligence case studies
Children’s Medical Research Institute
This Australian research institute embraces OCI Data Science to unlock flexibility and scalability, discover new insights, and perform analysis faster.
SS Global
SS Global, an innovative transportation logistics company, created an IoT application that monitors tire and vehicle conditions via a variety of sensors. They chose OCI Anomaly Detection to identify anomalies in vehicles, such as tire baldness or air leaks, which generate alerts to help prevent small issues from becoming big problems.
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Interested in learning more about Oracle AI? Let one of our experts help.
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They can answer questions such as
- How can I get started with AI and machine learning?
- Which services are right for me?