Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well. Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety of methods and to find and fix bugs. While participating in IARPA's Functional Map of the World challenge, we identified challenges along the entire deep learning pipeline and found various solutions to these challenges. In this paper, we present the performance, engineering, and deep learning considerations with processing and modeling data, as well as underlying infrastructure considerations that support large-scale deep learning tasks. We also discuss insights and observations with regard to satellite imagery and deep learning for image classification.

While big data analytics continue to grow in popularity among companies and organizations, their large-scale analytic implementations are often completed by software developers with little or no formal training in machine learning or data analysis. These developers are skilled at writing code but they do not have the understanding of the data analytics process to be efficient or necessarily accurate at it. These developers use processes and tools that are often ad hoc and incomplete as they learn by doing. We followed a development team through two analytics development cycles and analyzed their interactions with their data and tools. In this paper, we first describe the tools the developers used and then present concrete opportunities for the big data community to create tools that empower these developers to build more accurate analytics more efficiently.

Previous studies of team formation in multi‐agent systems have typically assumed that the agent social network underlying the agent organization is either not explicitly described or the social network is assumed to take on some regular structure such as a fully connected network or a hierarchy. However, recent studies have shown that real‐world networks have a rich and purposeful structure, with common properties being observed in many different types of networks. As multi‐agent systems continue to grow in size and complexity, the network structure of such systems will become increasing important for designing efficient, effective agent communities. We present a simple agent‐based computational model of team formation, and analyze the theoretical performance of team formation in two simple classes of networks (ring and star topologies). We then give empirical results for team formation in more complex networks under a variety of conditions. From these experiments, we conclude that a key factor in effective team formation is the underlying agent interaction topology that determines the direct interconnections among agents. Specifically, we identify the property of diversity support as a key factor in the effectiveness of network structures for team formation. Scale‐free networks, which were developed as a way to model real‐world networks, exhibit short average path lengths and hub‐like structures. We show that these properties, in turn, result in higher diversity support; as a result, scale‐free networks yield higher organizational efficiency than the other classes of networks we have studied.