andras/great-ai
1
0
Fork 0
Code Issues Pull requests 1 Projects Releases 4 Packages Wiki Activity Actions

Proofread first half of the thesis

Browse source
This commit is contained in:
Andras Schmelczer 2022-08-04 15:59:32 +02:00
parent 573ada2d63
commit 69f2f6dc2f
No known key found for this signature in database
GPG key ID: 0EA1BC97D0AB076E
14 changed files with 138 additions and 114 deletions
Download patch file Download diff file Expand all files Collapse all files

28
docs/thesis/chapters/appendix.tex
View file

@ -1,5 +1,30 @@
\appendix
\chapter{TAM questionnaire} \label{appendix:questions}
\chapter{Best practices assessment} \label{appendix:practices}
Similar to the approach of \cite{serban2020adoption}, participants are asked about their team's level of AI/ML deployment best practices adoption. The questions come from the entries of Tables \ref{table:best-practices-1} and \ref{table:best-practices-2} where \textit{GreatAI} was determined to provide a support level of \textit{Fully automated}.
\textbf{How well did the previous AI deployment that you have collaborated on implemented the following best-practices?} \textit{Each statement can be rated on a 5-point Likert scale or as ``Not applicable''.}
\begin{enumerate}
\item Write reusable scripts for data cleaning and merging
\item Make data sets available on shared infrastructure
\item Use versioning for data, model, configurations and training scripts
\item Continuously monitor the behaviour of deployed models
\item Log production predictions with the model's version and input data
\item Store models in a single format for ease of use
\item Equip with web interface, package image, provide REST API
\item Provide simple API for serving batch and real-time requests
\item Integration with existing data infrastructure
\item Querying, visualising and understanding metrics and event logging
\item Allow experimentation with the inference code
\item Keep the model's API and documentation together
\item Parallelise feature extraction
\item Cache predictions
\item Async support for top-down chaining models
\end{enumerate}
\chapter{Technology acceptance model questionnaire} \label{appendix:questions}
Following the methodology for parsimonious TAM of Wu et al. \cite{wu2011user}, each statement can be rated on a 7-point Likert scale.
@ -24,3 +49,4 @@ Following the methodology for parsimonious TAM of Wu et al. \cite{wu2011user}, e
\item Assuming \textit{GreatAI} is applicable to my task, I predict that I will use it on a regular basis in the future.
\item Overall, I intend to use the \textit{GreatAI} in my personal or professional projects.
\end{enumerate}