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

arXiv: https://arxiv.org/abs/1906.02287 · HTML: https://mlsanity.com/paper/1906.02287/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 29 of 29)

- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [AutoAI-TS: AutoAI for Time Series Forecasting](https://mlsanity.com/paper/2102.12347/) (2021)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models](https://mlsanity.com/paper/2408.00665/) (2024)
- [AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning](https://mlsanity.com/paper/2210.12090/) (2022)
- [Search Algorithms for Automated Hyper-Parameter Tuning](https://mlsanity.com/paper/2104.14677/) (2021)
- [Enabling SQL-based Training Data Debugging for Federated Learning](https://mlsanity.com/paper/2108.11884/) (2021)
- [Task Selection for AutoML System Evaluation](https://mlsanity.com/paper/2208.12754/) (2022)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [AutoML for Large Capacity Modeling of Meta's Ranking Systems](https://mlsanity.com/paper/2311.07870/) (2023)
- [Learning to Be A Doctor: Searching for Effective Medical Agent Architectures](https://mlsanity.com/paper/2504.11301/) (2025)
- [AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks](https://mlsanity.com/paper/2204.08358/) (2022)
- [A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks](https://mlsanity.com/paper/2102.11995/) (2021)
- [Automated Machine Learning Techniques for Data Streams](https://mlsanity.com/paper/2106.07317/) (2021)
- [Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives](https://mlsanity.com/paper/2407.11280/) (2024)
- [Evaluating Generic Auto-ML Tools for Computational Pathology](https://mlsanity.com/paper/2112.03622/) (2021)
- [Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction](https://mlsanity.com/paper/2306.07296/) (2023)
- [AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance](https://mlsanity.com/paper/2202.11928/) (2022)
- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)
- [Study on the effectiveness of AutoML in detecting cardiovascular disease](https://mlsanity.com/paper/2308.09947/) (2023)
- [A knowledge-driven AutoML architecture](https://mlsanity.com/paper/2311.17124/) (2023)
- [Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset](https://mlsanity.com/paper/2401.04666/) (2024)
- [Machine learning driven search of hydrogen storage materials](https://mlsanity.com/paper/2503.04027/) (2025)
- [A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization](https://mlsanity.com/paper/2504.07359/) (2025)
- [RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading](https://mlsanity.com/paper/2508.11338/) (2025)
- [A Review on Single-Problem Multi-Attempt Heuristic Optimization](https://mlsanity.com/paper/2509.26321/) (2025)

## Abstract (arXiv)

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.