The AI4Life project is dedicated to advancing the accessibility and utilization of computing infrastructures to facilitate the deployment and contribution of FAIR (Findable, Accessible, Interoperable, Reusable) AI-based models within the realm of life sciences research. 

Recognizing the challenges posed by the lack of reproducibility in current methodologies, particularly in computer vision, we advocate for the adoption of Docker containerization and Conda environment packaging to enhance the reproducibility of AI model deployment. Additionally, we are establishing seamless connections with cloud computational resources, commencing with a pilot implementation at renowned institutions such as EMBL-EBI and EMBL. These collaborations are instrumental in expanding our reach and leveraging cutting-edge computational capabilities to empower researchers in the life sciences domain.

To ensure the high availability and robustness of our services, we are configuring an infrastructure tailored for containerized deployment of web services. This infrastructure encompasses components such as BinderHub for executing Jupyter Notebooks, the BioEngine for serving models and applications from the BioImage Model Zoo effectively or the DL4MicEverywhere platform services. By standardizing the model and training data formats within Docker containers, we aim to streamline the process of connecting with EOSC resources.