I recently worked with Romeo to publish a developerWorks tutorial (“Build a cognitive IoT app in just 7 steps”) that brought together the roles of data scientist and IoT developer.  His tutorial introduces a reference architecture for implementing a cognitive IoT application and shows how to build a cognitive IoT app by using Watson IoT Platform and the IBM Data Science Experience to integrate machine learning in your solution.


Machine learning is fundamental for building cognitive IoT apps

 

Romeo believes that cognitive IoT is more than only advanced Human-Computer Interaction (HCI) using natural language processing and visual recognition. Cognitive IoT is automated decision making using advanced machine learning and neural-network-based artificial intelligence. He believes that machine learning is a critical capability that must be built into your IoT solutions because these solutions generate a vast amount of data (IoT is all about the data, right?) that must be stored, processed, and analyzed. Machine learning involves algorithms or models that are powerful enough to learn any required behavior just from the data.

Machine learning is a natural fit for IoT solutions, because it provides a natural technology for detecting anomalies and calculating forecasts – both prerequisites for automated decision making.  This Forbes article recently spoke of the interlock between IoT and machine learning,  and you can read more about machine learning in this white paper, “The democratization of machine learning,” which talks about the role that IBM and Apache Spark play in machine learning and about typical machine learning use cases. One such use case is using machine learning to help with the issue of IoT security, which is highlighted in this TechCrunch article. You can explore more about using IBM Data Science Experience and IBM Apache Spark and the Machine Learning Library in this tutorial in the Cloud Data Services dev center.

Machine learning is fundamental for building cognitive IoT appsRomeo is working on another developerWorks tutorial that takes machine learning to another level, a deeper level by applying deep learning on real-time anomaly detection of IoT sensor data. Simply put,  deep learning is a branch of machine learning using large neural networks.  (This Forbes article describes the intersection between artificial intelligence, machine learning, and deep learning.) While we wait for this next tutorial, check out Romeo’s Coursera course, “A developer’s guide to exploring and visualizing IoT data,” for a broader understanding of integrating machine learning into your cognitive IoT solutions. In addition, Romeo publishes his latest research regularly to his YouTube channel.

(If you want to explore other uses of machine learning, several Watson services take advantage of machine learning and deep learning.  You can explore how to use them in a chatbot in this blog post.)

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