Participants of the fourth edition of SHL recognition challenge 2021 aim to recognize eight locomotion and transportation activities in a user-independent manner based on radio data, including GPS reception, GPS location, WiFi reception, and GSM cell tower scans. Team "DD" proposes applying Google's AutoML Tables service to preprocess data, train, and evaluate the model. During this challenge, we showed the advantages and disadvantages of AutoML Tables. In addition, we have employed additional complimentary publically available datasets. AutoML Tables helped to train an artificial neural network model using the AdaNet algorithm. As a result, it has shown an ability to recognize classes with a precision of 81.2% and recall of 78.2%. Also, we opened the source code of the required feature engineering and published it on GitHub: https://github.com/dbalabka/shl-activity-recognition-2021. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; • Computing methodologies → Machine learning; Machine learning approaches; Neural networks.