AutoML (Automated Machine Learning) is a set of techniques and tools that automate the process of building machine learning models. This approach can help make machine learning more accessible to non-experts and reduce the time and effort required to develop effective models.

The wider adoption of AI and machine learning (ML) applications has been limited by the high costs of infrastructure and scarcity of ML experts and data scientists. To address some of these concerns, automated ML (AutoML) systems have been developed alongside cloud computing platforms to mitigate some of the constraints in the wider adoption of ML technologies, including by small and medium size organizations.[1]

Figure .1 Automated Machine Learning

Figure 1 shows AutoML (Automated Machine Learning) is a set of techniques and tools that automate the process of building machine learning models. The goal of AutoML is to make it easier for non-experts to develop machine learning models, and to reduce the time and effort required to build effective models.

AutoML encompasses a range of techniques and tools, including:

1. Automated model selection:This includes technologies like traffic sensors, cameras, and data analytics to monitor and manage traffic flow.
2. Hyperparameter optimization: These are vehicles equipped with technology to communicate with other vehicles and transportation infrastructure to optimize traffic flow and improve safety.
3. Automated feature engineering: This is a transportation concept that allows people to access a variety of transportation options through a single platform, such as a mobile app. Maas can include public transportation, ride-sharing services, bike-sharing, and car-sharing.
4. Neural architecture search: EVs are considered a smart mobility solution because they reduce carbon emissions and can be integrated into smart grids to manage energy demand.
5. AutoML platforms: Self-driving cars can be considered a smart mobility solution because they have the potential to improve safety, reduce congestion, and optimize the use of transportation infrastructure.

AutoML has several advantages over traditional machine learning approaches. It can significantly reduce the time and effort required to develop machine learning models, and can make it easier for non-experts to build effective models. Additionally, AutoML can help to identify the best model for a given problem, and can improve the performance of machine learning models by optimizing hyperparameters and input features.

However, AutoML is not a silver bullet and has some limitations. For example, it may not be suitable for all machine learning problems and may require substantial computational resources. Nonetheless, AutoML is a promising area of research and development, with the potential to democratize machine learning and make it more accessible to a wider range of users.

References:

1. https://www.tandfonline.com/doi/abs/10.1080/15228053.2022.2074585?journalCode=utca20

Cite this article:

Janani R (2023),Automated Machine Learning, Anatechmaz,pp.208