add some impl stuff
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\section{Implementation}\label{sec:implementation}
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\section{Implementation}\label{sec:implementation}
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\subsection{Dagster with Label-Studio}
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The main goal is to implement an active learning loop with the help of Dagster and Label-Studio.
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The task was split as much as possible...
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\subsection{Jupyter}
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To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
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This helps to choose which of the methods performs the best and which one to use in the final Dagster pipeline.
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A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet.
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\begin{lstlisting}
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# todo listing of the sample selection process
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\end{lstlisting}
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@ -8,6 +8,7 @@
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\usepackage{amsmath}
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\usepackage{amsmath}
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\usepackage{mathtools}
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\usepackage{mathtools}
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\usepackage{hyperref}
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\usepackage{hyperref}
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\usepackage{listings}
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\usepackage[inline]{enumitem}
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\usepackage[inline]{enumitem}
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\subsection{Material}\label{subsec:material}
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\subsection{Material}\label{subsec:material}
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\subsubsection{Dagster}
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\subsubsection{Dagster}
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Dagster is a data orchestrator for machine learning, analytics, and ETL workflows.
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It lets you define pipelines in terms of the data flow between reusable, logical components.
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Dagster is a tool that helps to build scalable and reliable data workflows.
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\subsubsection{Label-Studio}
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\subsubsection{Label-Studio}
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\subsubsection{Pytorch}
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\subsubsection{Pytorch}
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\subsubsection{NVTec}
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\subsubsection{NVTec}
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