MATLAB Production Server

 

MATLAB Production Server

Integrate MATLAB algorithms into web, database, and enterprise applications

MATLAB Production Server lets you incorporate custom analytics into web, database, and production enterprise applications running on dedicated servers or in the cloud. You can create algorithms in MATLAB, package them using MATLAB Compiler SDK, and then deploy them to MATLAB Production Server without recoding or creating custom infrastructure. Users can then access the latest version of your analytics automatically. Each algorithm, when deployed, can behave like a MATLAB function or as a web request handler.

MATLAB Production Server manages multiple MATLAB Runtime versions simultaneously. As a result, algorithms developed in different versions of MATLAB can be incorporated into your application.  The server runs on multiprocessor and multicore computers, providing low-latency processing of concurrent work requests. You can deploy the server on additional computing nodes to scale capacity and provide redundancy.

A MATLAB Production Server architecture diagram.

Production Deployment of MATLAB Analytics

Easily deploy analytics algorithms created by domain experts directly into production IT systems without recoding in a different language. The deployed analytics can be incorporated into a wide variety of enterprise applications accessed by large audiences, including web apps and Excel add-ins.

Multiple MATLAB Production Server instances behind a load balancer.

Scaling On-Premises or in the Cloud

MATLAB Production Server scales to handle multiple simultaneous requests through its stateless architecture. You can scale vertically by adding processor cores and memory, or horizontally by adding servers and a load balancer. Cloud reference architectures are available for Amazon® Web Services and Microsoft® Azure®. Alternately, deploy using containers to any Kubernetes cluster, hosted on-premise or from a cloud-managed service such as Amazon Elastic Kubernetes Service, Azure Kubernetes Service, or Google Kubernetes Engine.

Illustration showing MATLAB Production Server support for SSL/TLS client connections and encrypted code on disk.

Security and Encryption

Your MATLAB code is encrypted in-transit and at-rest. Industry standard certificate-based and token-based authentication and access control methods are available to protect the confidentiality of your MATLAB algorithms and data.

Illustration of the OSIsoft PI System reference framework.

Data Integration

Incorporate data from relational databases, NoSQL databases, and messaging engines. Stream asset and time-series data from operational systems such as OSIsoft® PI Asset Framework™ or Kafka clusters to flag anomalies, support predictive maintenance, and estimate the remaining useful life of assets.

Predictive maintenance application architecture showing stored motor sensor data, a desktop application, and server-based analytics.

Streaming Analytics

Ingest telemetry data from sensors and devices into your MATLAB analytics using connectors to event streaming and messaging engines such as Azure® IoT Hub, Azure Event Hubs, or Apache Kafka.

The MATLAB Production Server dashboard.

Management and Monitoring

MATLAB Production Server can be managed from either a web-based administration dashboard or the operating system command line. Review key system metrics from the dashboard to assess the health of your system and take preemptive action to improve response times or avoid bottlenecks.

A TIBCO Spotfire reference architecture.

Third-Party Visualization Applications

Visualize results from deployed MATLAB analytics in third-party visualization applications such as Tableau®, Spotfire®, Qlik®, and Power BI®.

Screenshot of a MATLAB web app that uses a MATLAB Production Server deployed function.

MATLAB Apps

Call functions deployed in MATLAB Production Server from any client app authored in MATLAB, including standalone desktop apps and web apps. Update your functions without redistributing the client app.

“MATLAB, MATLAB Production Server, and MathWorks Training Services enabled people on our risk team with conditional programming experience in C++ or Java to efficiently develop a core library for financial analysis and then deploy it as a web application, making it available to production systems in our enterprise environment.”