| The ubiquitous availability of heterogeneous sensor data created by Internet-of-Things (IoT) technologies and Industry 4.0 trends drastically accelerated the development of machine learning applications. AutoML services enable users with sparse machine learning knowledge to develop AI-based applications and rapidly evaluate the feasibility of data-driven ideas. Therefore, there exists a demand for holistic, low-code, end-to-end AutoML systems, which cover all stages of the machine learning lifecycle (i.e., feature engineering, model training, evaluation, versioning, provisioning, etc.). Although there are proprietary, cost-intensive platforms addressing these issues, no open-source solutions covering these aspects are known to us. In this paper we present AutoTiM, an open-source service capable of creating and operating highly performant machine learning models without requiring domain expertise or machine learning knowledge. |
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