From Auto-ML to Auto-GPT

Automated Machine Learning (Auto-ML) is an emerging technology that automates the tasks involved in building, training, and deploying machine learning models [1]. With the increasing ubiquity of machine learning, there is an ever-growing demand for specialized data scientists and machine learning experts. However, not all organizations have the resources to hire these experts. Auto-ML software platforms address this issue by enabling organizations to utilize machine learning more easily, even without specialized experts. 

Auto-ML platforms can be obtained from third-party vendors, accessed through open-source repositories like GitHub, or developed internally. These platforms automate many of the tedious and error-prone tasks involved in machine learning, freeing up data scientists' time to focus on more complex tasks. Auto-ML uses advanced algorithms and techniques to optimize the model and improve its accuracy, leading to better results.

One of the key benefits of Auto-ML is that it reduces the risk of human error. Since many of the tasks involved in machine learning are tedious and repetitive, there is a high chance of error when performed manually. Auto-ML automates these tasks, reducing the risk of human error and improving the overall accuracy of the model. In addition to reducing errors, Auto-ML also provides transparency by documenting the entire process. This makes it easier for researchers to understand how the model was developed and to replicate the process. Auto-ML can also be used by teams of data scientists, enabling collaboration and sharing of insights.

Furthermore, Auto-GPT is one of the popular tools for Auto-ML. It is a language model that uses deep learning to generate human-like text. Auto-GPT can be used for a range of natural language processing tasks, including text classification, sentiment analysis, and language translation. By automating the process of text generation, Auto-GPT enables researchers to focus on more complex tasks, such as data analysis and model deployment. This is just one example of how Auto-ML is revolutionizing the field of machine learning and making it more accessible to organizations of all sizes.

SEDIMARK aims to enhance data quality and reduce the reliance on domain experts on the data curation process. To accomplish this objective, the SEDIMARK team in the Insight Centre for Data Analytics of the University College Dublin (UCD) is actively exploring the utilization of Auto-ML techniques. By leveraging Auto-ML, SEDIMARK strives to optimize its data curation process and minimize the involvement of domain experts, leading to more efficient and accurate results.

[1] He, Xin, Kaiyong Zhao, and Xiaowen Chu. "AutoML: A survey of the state-of-the-art." Knowledge-Based Systems 212 (2021): 106622.