SAG_Workbench_880x440px_Feb21Internet of Things platforms are evolving quickly and machine learning is one of the most valuable tools on the workbench.

IoT adoption is growing; nearly 40% of businesses are deploying IoT solutions today and another 22% plan to start deploying IoT within the next two years, according to the Eclipse Foundation.

 As a result, IoT solutions are expected to evolve to help users gain greater value. Staying ahead of the rapidly shifting pace means that IoT platforms much continually evolve and innovate.

 Here at Software AG, our Cumulocity IoT team is devoted to continually innovating for existing users and for anyone using Cumulocity IoT – or any IoT platform – for the first time. 

 The newly released Cumulocity IoT 10.7 delivers an array of features and enhancements for greater efficiency and results. Among the key capabilities of is machine learning, the ability to use self-learning computer algorithms to project future trends, detect anomalies, and augment intelligence by ingesting images and video. 

 Machine learning workbench

The new machine learning workbench (MLW) in the upcoming 10.7 release allows you to build models directly within Cumulocity IoT. It is designed so that anyone can discover the potential of machine learning in just a few clicks by using one of our pre-trained models or use a drag-and-drop interface. 

Our platform will analyze provided data and identify the right model to use. Or, if you have a data scientist on hand who can build models with more freedom and flexibility, you can do that within the MLW through graphical tools such as Neural Network Editor and an embedded Jupyter Notebook capability.  

 With the MLW, we’ve highlighted critical capabilities in pre-trained models that will allow you to: 

  • Forecast based on time-series data
  • Ingest images and video to classify objects and identify defects
  • Prevent equipment downtime by predicting when equipment is likely to fail

Whether a model is created within Cumulocity IoT or imported from elsewhere, deployment is possible wherever needed in one click, either in the cloud or at the edge. 

 Staying ahead of new trends, we’re launching a new feature which allows users to deploy machine-learning models over a 5G network – to see results faster than ever before. Any device connected to a 5G network, whether it’s a smartphone or tablet, will be able to deploy models in one click and see it live instantly. 

Register below for our upcoming webinar on March 18th to interact with our product experts and watch demos and learn more about the new MLW and many other newly released features. 

Register here

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