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thesis/chapters/0_abstract.tex
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@ -6,8 +6,8 @@ Despite its long-standing history, artificial intelligence (AI) has only recentl
\absdiv{Objective}
This thesis investigates the causes of and a possible resolution to the asymmetry between the adoption of libraries for applying and deploying AI. The potential solution is validated through designing a software framework, called \textit{GreatAI}, which aims to facilitate \underline{G}eneral \underline{R}obust \underline{E}nd-to-end \underline{A}utomated \underline{T}rustworthy deployments while attempting to overcome the practical drawbacks of its predecessors.
\absdiv{Method}
The utility of \textit{GreatAI}'s design is validated by applying the principles of design science methodology through iteratively shaping it in a case study of a commercial text mining pipeline. Subsequently, interviews are conducted with ten practitioners to assess its generalisability.
\absdiv{Methods}
\textit{GreatAI} serves as a proxy for exploring the proposed design decisions, moreover, its initial focus is limited to the domain of natural language processing (NLP). Its design is validated by applying the principles of design science methodology through iteratively shaping it in two case studies of a commercial NLP pipeline. Subsequently, interviews are conducted with ten practitioners to assess its generalisability.
\absdiv{Results}
\textit{GreatAI} successfully helps implement 33 best practices through an accessible interface. These target the transition between the prototype and production phases of the AI development lifecycle. The feedback from professional data scientists and software engineers showed that ease of use and functionality are equally important in deciding to adopt deployment technologies, and the proposed framework was rated overwhelmingly positively in both dimensions.