In agricultural science, researchers face difficulties publishing and sharing their data-driven crop yield forecast models. The lack of direct and public access to the resulting models impedes collaboration among stakeholders, obscures relevant context and hinders continuous use of the contributions. In addition, researchers have to spend a significant amount of time on accessing, aggregating and pre-processing feature data from various sources. To address both issues, we present a platform that enables (i) public access to standardized and high-quality data by providing (ii) a uniform interface and SDK for data retrieval, as well as (iii) seamless hosting of data-driven crop yield prediction models. Our platform standardizes datasets, sourced from canonical providers such as Copernicus and ISRIC in the areas of observed and forecast weather, soil, and vegetation indices. We facilitate access to this data at different spatial and temporal resolutions through a uniform interface. Thereby, researchers can immediately focus on model development without having to manually gather data from heterogeneous sources. We further allow researchers to directly upload and deploy their crop yield prediction models by submitting their own code written in the Python programming language. The upload process is flexible, minimizing the integration effort on the researcher’s side. The uploaded models are deployed on the platform; historical as well as future prediction values are computed automatically and continuously. The predictions are visualized in an intuitive way, enabling direct comparison to other models and actual historical yield values as well as a direct assessment of models’ forecast capabilities. By publishing their models on our platform, researchers can increase their work’s visibility to actors from academia, media, politics, and business. This enables direct communication between the scientific community and the public, cultivating engagement, feedback and knowledge sharing. In summary, our platform revolutionizes the development and deployment of yield forecast models by providing a comprehensive suite of standardized data sets and the functionality for direct deployment of researchers’ models. This makes yield modeling seamless, collaborative and visible.