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docs/thesis/chapters/0_abstract.tex
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\begin{abstract}
\absdiv{Background}
Despite its long-standing history, artificial intelligence (AI) has only recently started enjoying widespread industry awareness and adoption, partly thanks to the prevalence of frameworks accessibly exposing state-of-the-art models. In order to achieve robust production deployments, the successful integration of AI components demands strong engineering methods. Concerningly, a tendency seems to be unfolding: even though professionals already have access to frameworks for deploying AI correctly, case studies and developer surveys have found that many deployments do not follow best practices.
Despite its long-standing history, artificial intelligence (AI) has only recently started enjoying widespread industry awareness and adoption, partly thanks to the prevalence of frameworks that accessibly expose state-of-the-art models. However, the transition from prototypes to production-ready AI services is still a source of struggle across the industry. Even though professionals already have access to frameworks for deploying AI correctly, case studies and developer surveys have found that many deployments do not follow best practices.
\absdiv{Objective}
This thesis investigates the reasons behind the asymmetry between the adoption of accessible AI libraries and existing reusable solutions to robust deployments. A software framework called \textit{GreatAI} is designed which aims to facilitate \underline{G}eneral \underline{R}obust \underline{E}nd-to-end \underline{A}utomated \underline{T}rustworthy AI deployments while attempting to overcome the practical drawbacks of its predecessors.
This thesis investigates the causes of and a possible resolution to the asymmetry between the adoption of accessible AI-libraries and reusable frameworks for AI deployments. The potential solution is validated through designing a library, 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} is validated by applying the principles of design science methodology through iteratively designing it in a case study of a commercial text mining pipeline. Subsequently, interviews are conducted with practitioners to validate the generalisability of the design.
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 for assessing its generalisability.
\absdiv{Results}
To do.
\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 library was rated overwhelmingly positively in both dimensions.
\absdiv{Conclusions}
To do.
Increasing the overall maturity of industrial AI deployments by devising APIs with ease of adoption in mind is proved to be feasible. Additionally, the created software framework was deemed effective and a candidate for showcasing the utility of following best practices.
\end{abstract}