2026-09-19T21:37:10Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1466592022-12-01T03:22:07Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Farahat, Amr
Golland, Polina
Yajamanam Kidambi, Sravani
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
2022-11-30T19:39:33Z
2022-11-30T19:39:33Z
2022-05
2022-08-25T19:15:58.576Z
https://hdl.handle.net/1721.1/146659
In this digital era, companies are battling at the forefront of innovation to share the next transformative idea with the world. Many of these ideas apply Artificial Intelligence (AI) to solve important societal problems. However, companies may be facing difficulties understanding the core problem, understanding the data, preparing the data, building models, evaluating and finally deploying the AI technologies.
In this thesis, we propose an AI Ecosystem that enables end-to-end AI lifecycle management. This ecosystem enables teams to easily transition from concept to prototype and from prototype to deployment. We show three key pillars for a successful AI project: process, people, and platform. We also discuss the ethical and regulatory considerations of building AI technologies in this space. The study was performed at Boston Scientific with two use cases: the interventional cardiology team actively developing an AI solution using Intervascular Ultrasound (IVUS) images and the supply chain team exploring AI solutions for demand forecasting. We demonstrate how an AI Ecosystem can enable such teams to focus on their core responsibility, developing innovative medical solutions that improve patients lives.
M.B.A.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
End-to-End Artificial Intelligence Lifecycle Management
Thesis
application/pdf
Master
Master of Business Administration
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="94282e11-ef3f-44eb-8ca4-61f3b94dec42">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>End-to-End Artificial Intelligence Lifecycle Management</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2022-05</PublicationDate>
<Authors>
<Author>
<DisplayName>Yajamanam Kidambi, Sravani</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>In this digital era, companies are battling at the forefront of innovation to share the next transformative idea with the world. Many of these ideas apply Artificial Intelligence (AI) to solve important societal problems. However, companies may be facing difficulties understanding the core problem, understanding the data, preparing the data, building models, evaluating and finally deploying the AI technologies.
In this thesis, we propose an AI Ecosystem that enables end-to-end AI lifecycle management. This ecosystem enables teams to easily transition from concept to prototype and from prototype to deployment. We show three key pillars for a successful AI project: process, people, and platform. We also discuss the ethical and regulatory considerations of building AI technologies in this space. The study was performed at Boston Scientific with two use cases: the interventional cardiology team actively developing an AI solution using Intervascular Ultrasound (IVUS) images and the supply chain team exploring AI solutions for demand forecasting. We demonstrate how an AI Ecosystem can enable such teams to focus on their core responsibility, developing innovative medical solutions that improve patients lives.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>