20 May Machine Learning and its Role in IoT Analytics
he key to value creation in the Internet of Things is the model. The model is used by both the app and analytics. It quantifies the value proposition, so the better the model, the higher the value. Developing these models in traditional markets is time consuming enough but given the volume, velocity and variety of IoT data, the load on the IoT data scientist can be overwhelming. Enter machine learning or ML for short. Machine learning can augment the skills of the data scientist by helping to select the algorithms or weighted ensemble of algorithms that provide the underlying structure for the model.
Here’s What We’ll Cover in this Video:
- The three main functions of IoT analytics.
- The differences between predictive and prescriptive analytics.
- The importance of keeping the model up to date as data changes.
- The two core steps to produce predictive intelligence.
- Predictive and simulative scoring.
- Why data scientists are moving from the IT departments and into the business units.
Watch this video to see Rob Patterson discuss how machine learning is being used to help create and maintain Internet of Things models.
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