19th AIAI 2023, 14 - 17 June 2023, León, Spain

AutoTiM - An Open-Source Service for Automated Provisioning and Operation of Time Series Based Machine Learning Models

Andre Ebert, Jakob Kempter, Marina Siebold, Robert Pesch, Tetyana Turiy, Tevin Tchuinkam, Thomas Caffin Sune


  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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