M2L Studio is a web-based vision system that makes it possible for the operators in a factory to configure and maintain a visual quality inspection camera or a sorting machine without any need for assistance from technicians. You can install M2L Studio on a cloud infrastructure of your choice or use MLPIOT to bring the application to the factory site and keep your data and the life-cycle of training and deploying the vision application secure inside your infrastructure.

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With the advent of powerful yet inexpensive edge computers it has become easy to utilise such computers for visual processing tasks. However, maintaining models on such devices in high numbers and multiple versions is a challenging problem. Microsoft IoT Edge allows cloud analytics and custom business logic to be deployed on devices so that organizations focus on business insights instead of data management. This will enable a solution to scale by configuring IoT software, deploying it to devices via standard containers, and monitoring it all from the cloud.

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Our eyes are trained to quickly determine if a surface is uniform or if any anomalous part can be found on it. Recent developments on deep learning and visual anomaly detection allow for mimicking the process that is performed by human eyes using smart computer programs. M2L Visual Anomaly Detection (VAD) identifies the part of the image that are inconsistent with the rest of it. It is an unsupervised technique based on latest advances in machine learning such as deep learning.

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