Images and video play a major role in scientific discoveries. Significant new advances in imaging science over the past two decades have resulted in new devices and technologies that are able to probe the world at nanoscales to planetary scales. These instruments generate massive amounts of multimodal imaging data. In addition to the raw imaging data, these instruments capture additional critical information--the metadata--that include the imaging context. Further, the experimental conditions are often added manually to such metadata that describe processes that are not implicit in the instrumentation metadata. Despite these technological advances in imaging sciences, resources for curation, distribution, sharing and analysis of such data at scale, are still lacking. Robust image analysis workflows have the potential to transform image-based sciences such as biology, ecology, remote sensing, materials science and medical imaging. In this context, this Chapter presents BisQue, a novel eco-system where scientific image analysis methods can be discovered, tested, verified, refined and shared amongst users on a shared, cloud based infrastructure. The vision of BisQue is to enable large-scale, data driven scientific explorations. The following sections will discuss the core requirements of such an architecture, challenges in developing and deploying the methods, and will conclude with an application to image recognition using deep learning.