Description
Chair: Prof. Dr. Marius Lindauer (LUH)
Description
Automated Machine Learning (AutoML) is concerned with automating the creation and deployment of machine learning models, enabling non-experts to harness the power of machine learning while also supporting experts by streamlining their workflows.
This session is divided into three parts. First, we will introduce AutoML, covering its core components and interfaces. We will explore how AutoML automates tasks like model selection, hyperparameter tuning, and neural architecture search, making machine learning more accessible to diverse industries while also improving efficiency for experts by automating repetitive and time-consuming tasks.
In the second part, we will begin with a brief live demonstration of AutoML tools for users with technical backgrounds. Following this, we will discuss "Green AutoML," an emerging research area focused on the environmental impact of automated machine learning. We will examine research in this field and explore ways to reduce the ecological footprint of machine learning technologies.
The final part will showcase a no-code AutoML web application, specifically designed to simplify machine learning for non-technical users. Through live demonstrations, we will show how AutoML empowers users in the renewable energy sector to quickly build and deploy models, driving innovation and sustainability in this critical domain.
Tutorial on the basics of AutoML and how it can support developers in more efficient development of AI applications
The adoption of specialized AI solutions like predictive maintenance, anomaly detection, and image classification is increasingly important across industries, driving operational efficiency and fostering innovation. However, this requires extensive AI expertise and programming skills. While large organizations can leverage dedicated data science teams, small and medium-sized enterprises (SMEs)...
