The process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model.
Published in Chapter:
General Model for Metrics Calculation and Behavior Prediction in the Manufacturing Industry: An Automated Machine Learning Approach
Maria João Lopes (Bosch Termotecnologia SA, Portugal & University of Aveiro, Portugal), Eugénio M. Rocha (University of Aveiro, Portugal), Petia Georgieva Georgieva (University of Aveiro, Portugal), and Nelson Ferreira (Bosch Termotecnologia SA, Portugal)
Copyright: © 2021
|Pages: 28
DOI: 10.4018/978-1-7998-6985-6.ch012
Abstract
In this chapter, a comprehensive description of a generic framework aimed at solving various predictive data-driven related use cases, occurring in the manufacturing industry, is provided. The framework is rooted in a general mathematical model so called queue directed graph (QDG). With the aid of QDGs and containerized microservices implementations, the typology of the system is analyzed, and real use cases are explained. The goal is for this framework to be able to be used with all use cases which fit in this typology. As an example, a data generation distribution model is proposed, the parameter stability and predictive robustness are studied, and automated machine learning approaches are discussed to predict the throughput time of products in a manufacturing production line just by knowing the processing time in their first stations.