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Andras Schmelczer 2022-10-01 11:15:28 +02:00
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docs/thesis/chapters/6_results.tex
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@ -71,10 +71,10 @@ Apply automation to trigger model retraining\textsuperscript{2}
% Employ Agile, DevOps-style workflows, allow automatic rollback\textsuperscript{2} & Docker Image, WSGI application & \checkmark \\\hline
% Deploy different versions of same application\textsuperscript{2} & Complex versioning support & $\sim$ \\\hline
Allow experimentation with the inference code\textsuperscript{3} & Development mode \& auto-reload & \checkmark\checkmark \\\hline
Keep the model's API and documentation together\textsuperscript{3} & Dashboard and Swagger & \checkmark\checkmark \\\hline
Keep the model and its documentation together\textsuperscript{3} & Dashboard and Swagger & \checkmark\checkmark \\\hline
Parallelise feature extraction\textsuperscript{3} & \texttt{parallel\_map} & \checkmark\checkmark \\\hline
Cache predictions\textsuperscript{3} & \texttt{@GreatAI.create} & \checkmark\checkmark \\\hline
Support asynchronous top-down chaining of models\textsuperscript{3} & All decorators support async & \checkmark\checkmark \\\hline
Allow robustly composing inference functions\textsuperscript{3} & All decorators support async & \checkmark\checkmark \\\hline
Implement standard schemas for common prediction tasks\textsuperscript{3} & \texttt{views} & \checkmark \\\hline
\end{tabular}}
@ -181,10 +181,10 @@ Secondly, the survey answers and, in general, the interviewees may be subject to
\section{Future work}
The primary purpose of the library was to serve as a proxy through which its design decisions could be tested and evaluated in their practical context. For this reason, its design aimed to be a proof-of-principle for validating hypotheses and answering research questions. After successfully doing that, it has been turned into a practical software library suitable for production-use\footnote{Available at \href{https://pypi.org/project/great-ai/}{pypi.org/project/great-ai} and \href{https://hub.docker.com/repository/docker/schmelczera/great-ai}{hub.docker.com/repository/docker/schmelczera/great-ai}.}.
The library's main limitations come from its bias toward NLP deployments. This is not unreasonable given the design's exploratory nature and the context of the case studies. Nevertheless, future work must focus on introducing and balancing support for many more fields' deployments. Although \textit{GreatAI} has already proved its utility, it has also shown that generalising and extending its functionality would be worthwhile. Therefore, many potential improvements are presented below.
\subsection{More ML fields}
\subsection{More ML domains and modalities}
The cases presented in Chapter \ref{chapter:case} revolved around NLP. This, of course, heavily influenced the design process. The two most notable effects can be found in the REST API's \texttt{/predict} endpoint and some \texttt{utilities} functions. The former is streamlined to accept JSON-compatible data (which caters to textual and tabular data), while the latter gives robust feature extraction support only for textual input. However, in practice, sound, image, and video are also widely taken as input. Furthermore, with the rise of multimodal models \cite{gao2020survey}, even different combinations of them may be simultaneously taken as input.