# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Background and Evolution of AutoML over the Years  
AutoML has evolved over the years since the introduction of ML models. According to [PERSON] et al. [33], [PERSON] laid the foundations of modern AutoML when he introduced the algorithm selection problem in 1976. [PERSON]'s work focused on the challenge of selecting the most appropriate algorithm for a given task based on the characteristics of the problem to be solved. This concept of algorithm selection is pivotal to AutoML, as it underpins the idea of automating the process of choosing the best model for a specific dataset [16; 34]. In the decades following [PERSON]'s work, machine learning witnessed substantial advancements, particularly in developing algorithms and computational techniques [35]. However, the evolution of AutoML gained momentum in the early 2000s with the growing recognition of the need for automation in machine learning. This era saw the introduction of more sophisticated techniques to reduce the manual effort required in model development [17; 36]. Early AutoML frameworks emerged, offering partial automation for tasks like model selection and hyperparameter optimization. These systems relied on heuristic methods and basic search algorithms to explore the space of possible models and configurations, laying the groundwork for more advanced AutoML solutions.  
The 2010s marked a significant turning point in the evolution of AutoML due to advances in computational power and the increasing availability of large datasets. Although the term \"AutoML\" cannot be attributed to a single author or reference, [PERSON] et al. [37] found that it first emerged in 2014 through the AutoML workshop co-hosted with the International Conference on Machine Learning (ICML) from 2014 to 2021. The introduction of Automated Machine Learning platforms, such as Google's AutoML and Microsoft's Azure Machine Learning, revolutionized the field by providing end-to-end solutions that could handle the entire machine learning pipeline [38; 39]. These platforms leveraged cloud computing resources to perform complex tasks such as neural architecture search (NAS), which automates the design of neural network architectures [40]. NAS techniques represented a breakthrough in AutoML, enabling the automatic generation of highly optimized deep-learning models.  
As AutoML continued to evolve, the focus shifted toward enhancing usability and accessibility. The late 2010s and early 2020s saw the proliferation of open-source AutoML frameworks, such as TPOT and H2O.ai's Driverless AI, which democratized access to advanced machine learning techniques [18]. These frameworks provided users with powerful tools to automate the entire model development process, from data preprocessing to model deployment. In addition, integrating AutoML with business intelligence platforms and data science workflows made it easier for organizations to adopt machine learning technologies, even without specialized expertise [41; 42]. Today, AutoML represents a mature and rapidly growing field that continues to evolve in response to the needs of both academia and industry. Modern AutoML platforms are characterized by their ability to handle increasingly complex data and model types and their focus on interpretability and transparency [19]. Researchers are actively exploring new methods to improve the efficiency and effectiveness of AutoML systems, including the use of meta-learning, transfer learning, and explainable AI techniques [43,44] These advancements are driving the adoption of AutoML across a wide range of applications, from healthcare and finance to marketing and manufacturing, where the ability to quickly and accurately build machine learning models is becoming a critical competitive advantage.