The Internet of Things generates massive volumes of data from millions of devices. Machine learning is powered by data and generates insight from it. Machine learning uses past behaviour to identify patterns and builds models that help predict future behaviour and events.
IoT and machine learning deliver insights otherwise hidden in data for rapid, automated responses and improved decision making. Machine learning for IoT can be used to project future trends, detect anomalies, and augment intelligence by ingesting image, video and audio.
Machine learning can help demystify the hidden patterns in IoT data by analyzing massive volumes of data using sophisticated algorithms. Machine learning inference can supplement or replace manual processes with automated systems using statistically derived actions in critical processes.
- Ingest and transform data into a consistent format
- Build a machine learning model
- Deploy this machine learning model on cloud, edge and device
Machine learning is a key component of Software AG’s Cumulocity IoT low-code, self-service IoT platform. The platform comes ready to go with the tools you need for fast results: device connectivity and management, application enablement and integration, as well as streaming analytics, machine learning, and machine learning model deployment. The platform is available on the cloud, on-premises and/or at the edge. Uniquely with Cumulocity IoT, standalone, edge-only solutions are also supported.
Machine Learning provides easy access to data residing in operational and historical data stores for model training. It can retrieve this data on a periodic basis and route it through an automated pipeline to transform the data and train a machine learning model. Data can be hosted on Amazon® S3 or Microsoft® Azure® Data Lake Storage, as well as local data storage, and retrieved using prebuilt Cumulocity IoT Data Hub connectors.
