Conveners
Processing Research Data with Artificial Intelligence and Machine Learning
- Marco Rorro (EGI.eu)
- Malgorzata Krakowian (EGI.eu)
Description
In the session, there will be presentations on various aspects of AI covering the entire MLOps lifecycle, from data acquisition, preprocessing, and labelling to inference pipelines. The projects AI4EOSC, iMagine, and interTwin will provide insights on key topics such as federated learning, distributed computing, experiment tracking, and drift detection. Use cases will demonstrate applications from composing inference pipelines to the application of Graph Neural Network and 3DGAN. The session will open with a presentation and discussion on the EGI AI roadmap, outlining future directions for advancing the AI capabilities of the EGI infrastructure.
Presentation materials
Aquatic ecosystems are vital in regulating climate and providing resources, but they face threats from global change and local stressors. Understanding their dynamics is crucial for sustainable use and conservation. The iMagine AI Platform offers a suite of AI-powered image analysis tools for researchers in aquatic sciences, facilitating a better understanding of scientific phenomena and...
The [iMagine][1] platform utilizes AI-driven tools to enhance the processing and analysis of imaging data in marine and freshwater research, supporting the study of crucial processes for ocean, sea, coastal, and inland water health. Leveraging the European Open Science Cloud ([EOSC][2]), the project provides a framework for developing, training, and deploying AI models. To effectively achieve...
The release of oil into marine environments can result in considerable harm to coastal ecosystems and marine life, while also disrupting various human activities. Despite advances in maritime safety, there has been a noticeable uptick in spill occurrences throughout the Mediterranean basin, as documented by the European Maritime Safety Agency's Cleanseanet program. Precisely predicting the...
Cloud computing has revolutionized how we store, process, and access data, offering flexibility, scalability, and cost-effectiveness. On the other hand, High Performance Computing (HPC) provides unparalleled processing power and speed, making it an essential tool for complex computational tasks. However, leveraging these two powerful technologies together has been a challenge.
In recent...
In recent years, the escalation of Extreme Weather Events (EWEs), including storms and wildfires, due to Climate Change has become a pressing concern. This exacerbation is characterised by increased intensity, frequency as well as the duration of such events.
Machine Learning (ML) presents a promising avenue for tackling the challenges associated with predicting global wildfire burned...
Researchers exploiting artificial intelligence (AI) techniques like machine learning and deep learning require access to specialized computing and storage resources. Addressing this need, the AI4EOSC project is providing an easy to use suite of services and tools within the European Open Science Cloud (EOSC). This platform aims to facilitate the development of AI models, including federated...
Managing and monitoring AI models in production, also known as machine learning operations (MLOps), has become essential in our days, resulting in the need for highly reliable MLOps platforms and frameworks. In the AI4EOSC project in order to provide our customers with the best available ones, we reviewed the field of open-source MLOps and examined the platforms that serve as the backbone of...
In recent years, Large Language Models (LLMs) have become powerful tools in the machine learning (ML) field, including features of natural language processing (NLP) and code generation. The employment of these tools often faces complex processes, starting from interacting with a variety of providers to fine-tuning models of a certain degree of appropriateness to meet the project’s needs.
This...
With the expansion of applications and services based on machine learning (ML), the obligation to ensure data privacy and security has become increasingly important in recent times. Federated Learning (FL) is a privacy-preserving machine learning paradigm introduced to address concerns related to data sharing in centralized model training. In this approach, multiple parties collaborate to...
The AI4EOSC project will deliver an enhanced set of services for the development of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models and applications for the European Open Science Cloud (EOSC). One of the components of the platform is the workload management system that manages execution of compute requests on different sites on EGI Federated Cloud.
To be...
Marine and coastal ecosystems (MCEs) play a vital role in human well-being, contributing significantly to Earth’s climate regulation and providing ecosystem services like carbon sequestration and coastal protection against sea level rise. However, they face serious threats, including one deriving from the interaction between multiple human stressors (e.g. pollution) and pressures more related...
Users may have difficulties to find the needed information in the documentation for products, when many pages of documentation are available on multiple web pages or in email forums. We have developed and tested an AI based tool, which can help users to find answers to their questions. The Docu-bot uses Retrieval Augmentation Generation solution to generate answers to various questions. It...
