Many organizations using MATLAB^® and Simulink^® to develop algorithms and models see an increased need to deploy, monitor, and manage them over their lifetime in production. DevOps refers to the set of capabilities needed to operationalize software applications, usually in an IT context. However, it is not straightforward for engineering algorithms and models; Gartner reports that more than 50% of data science projects do not result in business value due to problems with their operationalization. It is even more challenging when they involve physical systems.

DevOps for software and systems are needed by teams responsible for the operational performance of algorithms and models. Those teams often include engineering, software development, IT, and OT (Operation Technology). Engineers test, deploy, and debug their algorithms and models through the entire lifecycle, including redeploying algorithms after they are in operation. These production systems are typically owned by IT/OT.

In this session, learn how engineering teams are using MATLAB and Simulink product families to operationalize their algorithms and models and to bridge the gap with IT/OT teams.