Automated Retraining of Machine Learning Models

Kavikondala, Akanksha and Muppalla, Vivek and Krishna Prakasha, K and Acharya, Vasundhara (2019) Automated Retraining of Machine Learning Models. International Journal of Innovative Technology and Exploring Engineering, 8 (12). pp. 445-452. ISSN 2278-3075

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Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes

Item Type: Article
Uncontrolled Keywords: AutoML, Data preprocessing, Feature extraction, Feature selection, Hyperparameter, Machine Learning Optimization, Pipeline, Retraining, TPOT
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 27 Nov 2019 05:14
Last Modified: 27 Nov 2019 05:14

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