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Anomaly Detection

Oracle Cloud Infrastructure (OCI) Anomaly Detection is an AI service that 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—all without data science expertise.

Anomaly detection features

Proprietary anomaly detection algorithms

Oracle Anomaly Detection algorithms, backed by more than 150 patents, detect anomalies earlier with fewer false alarms. These algorithms work together to ensure higher sensitivity and better false alarm avoidance than other machine learning (ML) approaches, such as neural nets and support vector machines.

Blog: The fascinating (nuclear) history behind Oracle’s new anomaly detection service

Intelligent data preprocessing

OCI Anomaly Detection provides multiple data processing techniques that account for errors and imperfections in real-world input data, such as from low-resolution sensors. It automatically identifies and fixes data quality issues—resulting in fewer false alarms, better operations, and more accurate results.

Custom-trained models

APIs help developers upload raw data, train the anomaly detection model using their own business-specific data, and detect anomalies from the stored model. This makes creating highly accurate, custom-trained anomaly detection models accessible to everyone—even without data science experience.

Open-source options

Easy access to open-source technologies expands usage of OCI Anomaly Detection’s models. Pull time-series data from InfluxDB or streaming data from Apache Flink. Use open-source libraries like Plotly, Bokeh, and Altair for visualizations and to increase automation.

Ready-to-go results

OCI Anomaly Detection outputs include identified anomalies, ML model-based estimated values, and anomaly scores. Developers use these results to assess the severity of identified anomalies and automate business workflows to address them immediately.

Easy to integrate and deploy

OCI Anomaly Detection is a multitenant service over public REST APIs. Developers can deploy a scalable anomaly detection service easily without in-house data science and ML support, all with the lowest cost platform for data networking, storage, and egress.

Scalability on demand

OCI Anomaly Detection automatically scales for training and detection needs across all data sources and loads. Developers can now focus on creating applications and solutions to achieve their business goals, without worrying about the infrastructure.

SS Global

Improving vehicle uptime and on-time cargo delivery

SS Global, an innovative transportation logistics company, created an IoT application which 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.

Anomaly detection use cases

  • IT operations use cases

    OCI Anomaly Detection helps IT teams improve service levels, root cause analysis, IoT deployments, threat reduction, and database transaction monitoring.

  • Business operations use cases

    From fraud detection for banks to funnel conversion for marketing teams, OCI Anomaly Detection enables organizations to discover issues and opportunities to improve their business processes’ innovation and efficiency.

  • AI and ML operations use cases

    OCI Anomaly Detection improves AI and ML processes, including apps monitoring, data cleansing, and data training. Use anomaly detection to discover unexpected changes in model accuracy, improve data integrity, and optimize model and application performance.

  • Finance and retail: Fraud detection

    Fraud patterns change over time, and traditional deep-learning methods don’t always detect rare events in very large data sources. Specialized algorithms can identify fraudulent transactions immediately—catching fraudsters in real time, with fewer false alarms, than other ML approaches.

  • Utilities: Energy management

    Utility companies must monitor energy production and consumption in real time to dynamically respond to demand and to optimize energy consumption. Innovative ML approaches analyze energy production, weather, and control-systems data to deliver an optimal experience for both energy producers and consumers

  • Manufacturing: Operational efficiency

    Anomaly detection of operational metrics in real time—such as yield, utilization, and throughput—can identify undesirable changes in production and generate automated workflows for immediate action.

  • Transportation and manufacturing: Equipment monitoring

    Breakdowns in equipment mean lost productivity and even risk to employees. Fast detection and root cause analysis for parts and machinery keep systems running smoothly.

Anomaly Detection pricing

Always Free Tier for developers: OCI Anomaly Detection is part of OCI’s Always Free Tier for developers and data scientists to assess and train their models.

Production: For ongoing operations, the OCI Anomaly Detection service reduces costs by up to 20 percent when compared to other clouds. Pricing is based on a grouping of 1,000 transactions, defined as 1,000 detect API calls, where each call processes up to 1,000 data points. If a customer calls OCI Anomaly Detection with a payload of 500 data points that would count as one transaction. OCI Anomaly Detection offers zero-cost pricing for the first 1,000 production transactions every month. Each grouping of 1,000 transactions following the first 1,000 transactions is billed at $0.25 per 1,000 transactions.

OCI Anomaly Detection

Number of calls
Tier limit
0–1,000 transactions

1,000 transactions
Every 1,000 transactions after the first 1,000 transactions


OCI Anomaly Detection Training

Number of calls
Tier limit
Training is free up to a total of 100M data points (product of signals and time stamps) per region per month. Customer can file a service request ticket to increase this training limit.
$0.00 (Always Free Tier)
Only for model training purposes
July 13, 2021

Deploy remote diagnostics without in-house data science and ML experts

Viji Krishnamurthy, Senior Director Product Management, Oracle

OCI Anomaly Detection is a robust, scalable and user-friendly AI service that watches large volume multivariate time series data and alerts you when something warrants your attention. Authenticated users can access OCI Anomaly Detection Service—part of our public cloud offering—via REST API, command-line interface, development kit, or the Oracle Cloud Infrastructure console.

Related cloud products

OCI Data Science

Open source algorithms and frameworks

OCI Data Integration

Combine and transform data for data science and analytics

OCI Data Catalog

Find and govern data using an organized inventory across the enterprise

OCI Data Flow

Run Apache Spark applications on a fully managed big data service

Get started with Anomaly Detection

Perform a free build in OCI Anomaly Detection

Build an ML model in an OCI workshop. Detect production anomalies in just a few guided steps.

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