An overview of intelligent data processing methods in the systems of the Industrial Internet of Things is presented in this paper. A comparison of Big Data analysis methods in industrial systems with a significant load is provided. The methods of distributed machine learning for data processing are offered. A software model for data analysis of different volumes is developed in the work. The analysis of the basic approaches to the organization of machine learning is carried out: federal and undistributed. The effectiveness of the use of federal machine learning was experimentally proven, as it provides higher accuracy of data processing, even when increasing their volume. It is determined that unallocated machine learning works faster, so it can be used in systems where fast data processing is a priority. This approach opens up the possibility of creating an adaptive model of the Industrial Internet of Things system that can self-learn and adjust its infrastructure depending on changing parameters.
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