In the previous chapter, we investigated some of the most interesting classes and use cases to do with I-IoT analytics. We discovered that an analytic can be descriptive, diagnostic, predictive, or prognostic. We also mentioned the remaining useful life (RUL) of an asset or part within the I-IoT.

In this chapter, we will improve our knowledge of I-IoT analytics, using more advanced technologies based on machine learning (ML) and deep learning (DL).

In this section, we will explore the following:

• Digital twins
• Practical examples with DL and ML algorithms
• Platforms on which to build digital twins