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moved the images to the appendix
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% There are also unsupervised learning methods that do not have a fixed number of classes. While this method would work as an expandable model method, it would not work for the purpose of this project. This project requires that the model has a specific set of labels which does not work with unsupervised learning which has unlabelled data. Some technics that are used for unsupervised learning might be useful in the process of creating expandable models.
\pagebreak
\section{Problem Analysis \& Design Choices}
\section{Problem Analysis}
\subsection{Structure of the Service}
The system needs to manage:
@ -183,7 +183,7 @@
\item{Running of models}
\end{itemize}
The system is designed with a semi-monolithic approach. The management of the data, and generation of the models will be done in the monolith while the training/running of the models will be done in GPU dedicated nodes.
The system is designed with a semi-monolithic approach \ref{fig:expandable_models_simple}. The management of the data, and generation of the models will be done in the monolith while the training/running of the models will be done in GPU dedicated nodes.
The overall workflow of a user who wants a model created would be:
\begin{itemize}
@ -192,11 +192,8 @@
\item{The user requests the classification or confirmation of an image}
\end{itemize}
% TODO add diagram!
\includegraphics[height=\textheight]{expandable_models_simple}
\subsection{Generation Models}
The system requires the generation of models. Generating all models based on one single model would decrease the complexity of the system, but it would not guarantee success.
The system requires the generation of models \ref{fig:expandable_models_generator}. Generating all models based on one single model would decrease the complexity of the system, but it would not guarantee success.
The system needs to generate successful models, to achieve this, the system will be performing two approaches:
\begin{itemize}
@ -212,13 +209,9 @@
Since the AutoML approach would be more computational intensive, it would be less desirable to run. Therefore, the approach would be for the database search to happen first, where known possibly good models would be first tested. If a good model is found, then the search stops and if no model is found, the system would resort to AutoML to find a suitable model.
% TODO add diagram
\includegraphics[height=\textheight]{expandable_models_generator}
\pagebreak
\section{Design Choices}
@ -246,8 +239,24 @@
% The smaller models would all be all generated by the platform, this model's purpose would be actually classification.
% This approach would offer a lot of expandability, as it makes the addition of a new class as easy as creating a new small model.
\appendix
\newpage
\pagebreak
\section{Appendix}
\begin{figure}
\begin{center}
\includegraphics[height=\textheight]{expandable_models_simple}
\end{center}
\caption{Contains an overall view of the entire system}\label{fig:expandable_models_simple}
\end{figure}
\begin{figure}
\begin{center}
\includegraphics[height=\textheight]{expandable_models_generator}
\end{center}
\caption{Contains an overall view of the model genration system}\label{fig:expandable_models_generator}
\end{figure}
\pagebreak
\section{References}