Automated Machine Learning: State-of-The-Art and Open Challenges

By Radwa El Shawi, Mohamed Maher, Sherif Sakr. 2019

205 citations recorded by the source corpus.

With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.

Subfields: Artificial Intelligence, Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Neural Architecture Search & AutoML

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Elshawi et al. (2019) and He et al. (2019) focus on automatic neural network architecture search—which is not covered in this survey—and only briefly introduce methods for classic machine learning.”
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning (2020, methodology): “To alleviate this problem, we take advantage of automated machine learning (AutoML)(He et al., 2019; Z¨oller and Huber, 2019; Elshawi et al., 2019) to automatically adapt hyper parameters.”
Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results (2021, methodology): “‘min_samples_leaf’:[1, 2, 3], ‘min_samples_leaf’: 3,”
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “The aim of HPO is to achieve optimal or near-optimal model performance by tuning hyper-parameters within the given budgets [3].”