2026-09-19T04:57:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/914412026-06-06T00:54:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Una-May O'Reilly and Erik Hemberg.
Emeagwali, Ijeoma
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-11-04T21:36:55Z
2014-11-04T21:36:55Z
2014
2014
http://hdl.handle.net/1721.1/91441
893676027
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
3
Cataloged from PDF version of thesis.
Includes bibliographical references (page 57).
This thesis describes how to build a flow for machine learning on large volumes of data. The end result is EC-Flow, an end to end tool for using the EC-Star distributed machine learning system. The current problem is that analysing datasets on the order of hundreds of gigabytes requires overcoming many engineering challenges apart from the theory and algorithms used in performing the machine learning and analysing the results. EC-Star is a software package that can be used to perform such learning and analysis in a highly distributed fashion. However, there are many complexities to running very large datasets through such a system that increase its difficulty of use because the user is still exposed to the low level engineering challenges inherent to manipulating big data and configuring distributed systems. EC-Flow attempts to abstract a way these difficulties, providing users with a simple interface for each step in the machine learning pipepline.
by Ijeoma Emeagwali.
M.Eng.
57 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Using distributed machine learning to predict arterial blood pressure
Thesis
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