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Machine Learning in IoT Devices

Machine learning plays a key role in interpreting the hidden patterns in IoT. Using advanced algorithms, machine learning analyzes massive data volumes. Machine learning has automated all processes using statically derived actions in crucial processes.

You can now use machine learning to build models for machine learning, ingest and interpret data and deploy models to the cloud on the device and edge.

Benefits of Machine Learning in IoT

Ease of training machine learning model To help you build a machine learning model easier AutoML empowered systems like Cumulocity allows the perfect machine learning model to be used based on the data available.
Flexibility in data science library choiceOut of many varieties of data science to choose from like Keras, Tensorflow® and Scikit-learn, machine learning models can be developed in a data science framework that suits you.
Fast operationalisation of machine learning Whether at the edge or in the cloud, model deployment into a production environment is fast. This way they can be easily supervised and updated in case of any pattern shift. To enhance adoption the already trained data models are available for instant model deployment.
Prefabricated connectors for datastores (historical and operational)IoT machine earning has provided accessible data in both historical and operational data stores for the training of models. Data is then hosted on S3 or Microsoft®, Amazon®, Azure® Data Lake Storage and local data storage.

Topics in Machine Learning

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