If you are considering implementing an IIoT and machine learning (ML) solution for predictive maintenance, process optimization, or production yield improvement, you are likely to face these common challenges: 1) Connecting the solution to legacy edge devices and OT systems such as PLC's, SCADA, and asset condition monitoring systems, industrial data historians etc. and adding new sensors , ERP, and CMMS systems. 2) Deploying machine learning solutions requires extensive data science expertise or expensive consulting. 3) Integrating a complete solution with modern infrastructure like the cloud. 4) Deploying pilot projects with ease before scaling to the enterprise.

The Quartic Platform® is an end-to-end IIoT and ML platform that lets you connect to your existing legacy automation infrastructure and edge devices. Its industrial data pipeline is built to give context to abstract data in the language of the industrial asset while its data engine automates machine learning and presents it in a language SMEs understand--promoting in-house ML solutions. With a highly interpretable ML, the Quartic Platform increases adoption, trust, and sustainability of industrial AI and allows you to easily integrate ML with real-time data to define business and control logic, making AI actionable. The intelligence created using the Quartic Platform® can be connected back to your existing OT systems like DCS and OSI PI historian so your operations and maintenance staff can continue to use existing workflows and tools.

Comprising of two main components, each with a set of application modules, the Platform can be deployed in a hybrid Edge-Fog-Cloud architecture on Azure.

While built using the most modern and powerful AI techniques and technology, the differentiation of the Platform is its industrially focused design. Crafted for OT professionals and industrial subject matter experts, it empowers them to build IIoT systems compliant with IIRA (ISO/IEC/IEEE42010:2011) and RAMI 4.0 with very little training or culture change. With just a few clicks, it connects to data stranded in legacy equipment and puts it in the context of operations. With all data given the same context, on-line deployment and scaling is accelerated.