Machine Learning (ML) for IoT
            

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 behavior to identify patterns and builds models that help predict future behavior and events.

What is machine learning?

Machine learning delivers insights hidden in IoT 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.

Why use machine learning for IoT?
Machine learning can help demystify the hidden patterns in IoT data by analyzing massive volumes of data using sophisticated algorithms. It can supplement or replace manual processes with automated systems using statistically derived actions in critical processes.

Sample use cases
Companies are utilizing machine learning for IoT to perform predictive capabilities on a wide variety of use cases that enable the business to gain new insights and advanced automation capabilities.

With machine learning for IoT, you can:

  • Ingest and transform data into a consistent format
  • Build a machine learning model
  • Deploy this machine learning model on cloud, edge and device

For example, using machine learning, a company can automate quality inspection and defect tracking on its assembly line, track activity of assets in the field and forecast consumption & demand patterns.

Benefits of machine learning for IoT

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.

Simplify machine learning model training
Cumulocity IoT Machine Learning is designed to help you quickly build new machine learning models in an easy manner. AutoML support allows the right machine learning model to be chosen for you based on your data, whether that be operational device data captured on the Cumulocity IoT platform or historical data stored in big data archives.

Flexibility to use your data science library of choice
There are a wide variety of data science libraries available (e.g., Tensorflow®, Keras, Scikit-learn) for developing machine learning models. Cumulocity IoT Machine Learning allows models to be developed in data science frameworks of your choice. These models can be transformed into industry-standard formats using open source tools and made available for scoring within Cumulocity IoT.

Rapid model deployment to operationalize machine learning quickly
Whether created within Cumulocity IoT Machine Learning itself or imported from other data science frameworks, model deployment into production environments is possible wherever needed in one click, either in the cloud or at the edge. Operationalized models can be easily monitored and updated if underlying patterns shift. Additionally, pretrained and verified models are available for immediate model deployment to accelerate adoption.

Prebuilt connectors for operational & historical datastores
Cumulocity IoT Machine Learning provides easy access to data residing in operational and historical datastores 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 DataHub connectors.

Integration with Cumulocity IoT Streaming Analytics
Cumulocity IoT Machine Learning enables high-performance scoring of real-time IoT data within Cumulocity IoT Streaming Analytics. Cumulocity IoT Streaming Analytics provides a “Machine Learning” building block in its visual analytics builder that allows the user to invoke a specified machine learning model to score real-time data. This provides a no-code environment to integrate machine learning models with streaming analytics workflows.

Notebook integration
Jupyter Notebook, a de facto standard in data science, provides an interactive environment across programming languages. They can be used to prepare and process data, train, deploy and validate machine learning models. This open-source web application is integrated with Cumulocity IoT Machine Learning.

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