Project Wenju is an initiative focused on the acceleration of production AI. It advocates a holistic and engineering approach to enterprise AI, facilitates the end-to-end integration of production AI systems, streamlines the lifecycle management of AI solutions, and significantly reduces skills requirements and time to value.
Specifically, Wenju seeks to tackle the following challenges in production AI:
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The Challenge of Infrastructure: A company’s ultimate success with AI depends on how suitable its infrastructure is for its AI applications. Provisioning and managing AI infrastructure requires key insights for technology selection, topology design, configuration engineering, system interoperation, and resource optimization. It must be performed expediently and effectively in order to meet the business needs and maximize the return-on-investment of AI initiatives.
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The Challenge of Data: Data is the fuel that powers AI, because machine learning algorithms count on extremely large datasets to reveal patterns, trends, and associations. Big data, as it is popularly called, has four important attributes: volume, velocity, variety, and veracity. These four V’s have given rise to many problems such as data quality, data heterogeneity, data silos, data cataloging, data consistency, data performance, and data privacy. If not adequately addressed, these problems will hinder the timely delivery of big data’s true value, the fifth V, by AI systems.
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The Challenge of Skills: Although AI is intended to automate things as much as possible, the development of AI itself requires extensive human engagement, not counting the new blue-collar job of data labeling. AI development requires new skills of data science and machine learning. In addition, software engineers have to relearn a lot of what they take for granted about how to program. AI-related skills are rare and in high demand. The general shortage of skilled resources in the industry calls for reducing the entry barriers to AI.
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The Challenge of Trust: Broad adoption of AI will heavily depend on the ability to trust the behavior and output of AI systems. People need assurance that AI is reliable and accountable to people, can explain its reasoning and decision-making, will cause no harm, and will reflect the values and norms of our societies in its outcomes. There is currently a substantial trust gap for AI, which is obstructing an effective path for economic growth and societal benefit.
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The Challenge of Operationalization: Operationalizing one machine learning model may not be a big deal, but it is a completely different beast to consistently and effectively operationalize hundreds of AI applications in an enterprise, where the applications are frequently updated and stringent service-level objectives in terms of availability, performance, and prediction quality must be met. In addition, AI systems consist of software, data, and learning components that are intertwined. The interdependencies among those components complicate the continuous integration and delivery of end-to-end AI systems.