From 87ecf8925262aab78ed8098b1438abadd0a2ee1e Mon Sep 17 00:00:00 2001
From: TobbeCarlsson <76130967+TobbeCarlsson@users.noreply.github.com>
Date: Thu, 12 Sep 2024 13:19:31 +0200
Subject: [PATCH] Week 4: Scientific proposal (#2464)

* PR for scientific paper

* added title
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+# Assignment Proposal
+
+## Title
+
+Machine Learning Operations (MLOps): Overview, Definition, and Architecture
+
+
+## Names and KTH ID
+
+  - Tobias Carlsson (tocarls@kth.se)
+  - Johann Biörck (jbiorck@kth.se)
+
+## Deadline
+
+- Week 4
+
+## Category
+
+- Scientific-paper
+
+## Description
+We want to discuss the article "Machine Learning Operations (MLOps): Overview, Definition, and Architecture" (https://ieeexplore.ieee.org/abstract/document/10081336) 
+
+The paper we want to present discusses how many ML products and endeavors fail to deliver on their expectations and how MLOps address that issue. The paper tries to adress the gap that currently exists in MLOps and claiming that it's still a very vague and ambiguous term. The paper presents and overview of the necessary principles, components and roles of MLOps and furthermore tries to provide a clear definition of the term to provide guidence to future ML researchers and practitioners who wish to automate their ML products.
+
+
+**Relevance**
+
+
+With devops as a concept being fairly young (started around 07',08') and the explosion of ML in recent years, standard practices and terminology is still very ambigous and unclear. This paper aims to reduce confusion and aid future research within ML and MLOps.