In this year of Rudolf Kalman's demise, this article is dedicated to his memory. We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same method can be used for classification and regression (including forecasting), offline and real-time applications and simple and hard ML problems. We have achieved our objective in the form of State-space Recurrent Kernel-projection Time-varying Kalman or "RKT-Kalman" method.

In an earlier blog, "Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation", I introduced the need for Dynamical ML as we now enter the "Walk" stage of "Crawl-Walk-Run" evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two during supervised "Training" and use this static map for business purposes during "Operation". I made the following points using IoT as an example. Dynamical ML solution involves State-Space data model (more below). What more does a Dynamical ML solution offer?

Dynamical ML is machine learning that can adapt to variations over time; it requires "real-time recursive" learning algorithms and time-varying data models such as the ones described in the blog, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.

Dynamical ML is machine learning that can adapt to variations over time; it requires "real-time recursive" learning algorithms and time-varying data models such as the ones described in the blog, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.

What is normal for a machine (or the System under observation) is environment-dependent and ever-changing in real life!

In an earlier blog, "Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation", I introduced the need for Dynamical ML as we now enter the "Walk" stage of "Crawl-Walk-Run" evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two during supervised "Training" and use this static map for business purposes during "Operation". I made the following points using IoT as an example. Dynamical ML solution involves State-Space data model (more below). What more does a Dynamical ML solution offer?

In an earlier blog, "Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation", I introduced the need for Dynamical ML as we now enter the "Walk" stage of "Crawl-Walk-Run" evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two during supervised "Training" and use this static map for business purposes during "Operation". I made the following points using IoT as an example. Dynamical ML solution involves State-Space data model (more below). What more does a Dynamical ML solution offer?

In this year of Rudolf Kalman's demise, this article is dedicated to his memory. We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same method can be used for classification and regression (including forecasting), offline and real-time applications and simple and hard ML problems. We have achieved our objective in the form of State-space Recurrent Kernel-projection Time-varying Kalman or "RKT-Kalman" method.