Next location prediction is a discipline that involves predicting a users next location. Its applications include resource allocation, quality of service, energy efficiency, and traffic management. This paper proposes an energy-efficient, small, and low parameter machine learning (ML) architecture for accurate next location prediction, deployable on modest base stations and edge devices. To accomplish this we ran a hundred hyperparameter experiments on the full human mobility patterns of an entire city, to determine an exact ML architecture that reached a plateau of accuracy with the least amount of model parameters. We successfully achieved a reduction in the number of model parameters within published ML architectures from 202 million down to 2 million. This reduced the total size of the model parameters from 791 MB down to 8 MB. Additionally, this decreased the training time by a factor of four, the amount of graphics processing unit (GPU) memory needed for training by a factor of twenty, and the overall accuracy was increased from 80.16% to 82.54%. This improvement allows for modest base stations and edge devices which do not have a large amount of memory or storage, to deploy and utilize the proposed ML architecture for next location prediction.

The story is about the birth of Machine Learning architecture and how each of the three architects developed their own AI style. We tried to figure out how to teach AI to make architecture without mimicking existing styles.

What is Machine Learning Architecture? I'm looking for a new job in Data Science, and have found job positions titled as "Machine Learning Architect". Don't really sure about what it is, but maybe I am still a newbie. What does a Machine Learning Architect do and what are the required skills? Can you give examples, please?