The Minds Mastering Machines conference will take place on May 20 and 21, 2025. Until November 28, 2024, the organizers, iX and dpunkt.verlag, are looking for presentations for this conference focused on Machine Learning and AI. The main focus is on practical applications of Machine Learning rather than the AI hype. In companies, AI still largely involves training neural networks and using classical ML methods. Presentations on current AI topics are always welcome.
The conference targets professionals with a technical focus who implement ML projects into technical reality. This includes data scientists, data engineers, software developers, and software architects. On May 19, the day before the conference, full-day workshops are planned.
The Call for Proposals is open until November 28, and the organizers are seeking 40-minute presentations and full-day workshops on topics such as:
- Deep Learning
- GenAI in practice
- LLMs and multimodal models
- Validation of ML applications
- Data engineering from training to production
- Efficient models
- Data protection, ethics, and law
Experience reports are particularly welcome. The program will be published in mid-January. Those who wish to stay informed about the conference can subscribe to the newsletter.
Machine Learning is increasingly important in today’s technology-driven world. It involves using algorithms and statistical models to enable computers to perform tasks without explicit instructions. This technology is applied in various fields, such as healthcare, finance, and marketing, to improve efficiency and outcomes.
Deep Learning, a subset of Machine Learning, uses neural networks with many layers to analyze various factors of data. This method is crucial for tasks like image and speech recognition. Large Language Models (LLMs) are another significant area, focusing on understanding and generating human language.
Data engineering is the process of preparing data for analysis. It involves collecting, cleaning, and transforming data into a usable format. This step is essential for ensuring that ML models can make accurate predictions.
Efficient models are designed to perform tasks using minimal resources. This efficiency is vital in real-world applications where computational power and time are limited.
Data protection, ethics, and law are critical considerations in Machine Learning. As ML models often handle sensitive information, ensuring data privacy and adhering to ethical standards is crucial. Legal frameworks guide how data can be used, ensuring that individuals’ rights are protected.
Overall, the Minds Mastering Machines conference aims to bring together experts to share knowledge and advancements in Machine Learning. It provides a platform for discussing practical applications and challenges, promoting innovation and collaboration in the field.