Predictive and Preventive Maintenance: Using state-of-the-art Machine Learning techniques (including Deep Learning) for predicting machine failures and/or operational issues will in advance of the actual failure. This will help avoid the machine downtime, and also do the maintenance as and when required instead of a fixed scheduled maintenance.
Real-time analytics: Real-time discovery of patterns and trends in the machine operations, and real-time dashboards for monitoring machine health, operational efficiency, and performance parameters.
Process and Efficiency Improvements: By observing the machine data data over a longer period of time, we can identify patterns and causal relationships, identify losses of efficiency (for example, unnecessary power consumption) and performance bottlenecks, and recommend remedies to eliminate any efficiency and performance losses. The net result is improved operational efficiency, reduced power consumption, and reduced costs.
Edge Computing: Edge computing is the emerging trend in IoT. For advanced analytics using unstructured data such as audio-visual data, sending the raw data to cloud infrastructure and getting back the results from cloud could lead to significant delays. This makes any real-time analytics using such unstructured data over cloud computing platform infeasible. Edge computing addresses this by using powerful tiny computers at the "edge" of the IoT network (i.e. and the point of sensor deployment), and we run our powerful AI tools and feature extraction techniques on those computers locally, without any need of communication to the cloud or to any other server. The net result is an instant, real-time analytics based on audio-visual data with the results displayed right then and there, all at the "Edge" without any communication bottlenecks!