TY - GEN
T1 - Operating Enterprise AI as a Service
AU - Casati, Fabio
AU - Govindarajan, Kannan
AU - Jayaraman, Baskar
AU - Thakur, Aniruddha
AU - Palapudi, Sriram
AU - Karakusoglu, Firat
AU - Chatterjee, Debu
PY - 2019
Y1 - 2019
N2 - This paper discusses the challenges in providing AI functionality “as a Service” (AIaaS) in enterprise contexts, and proposes solutions to some of these challenges. The solutions are based on our experience in designing, deploying, and testing AI services with a number of customers of ServiceNow, an Application Platform as a Service that enables digital workflows and simplifies the complexity of work in a single cloud platform. Some of the underlying ideas were developed when many of the authors were part of DxContinuum inc, a machine learning (ML) startup that ServiceNow bought in 2017 with the express purpose of embedding ML in the ServiceNow platform. The widespread adoption of ServiceNow by the majority of large corporations has given us the opportunity to interact with customers in different markets and to appreciate the needs, fears and barriers towards adopting AIaaS and to design solutions that respond to such barriers. In this paper we share the lessons we learned from these interactions and present the resulting framework and architecture we adopted, which aims at addressing fundamental concerns that are sometimes conflicting with each other, from automation to security, performance, effectiveness, ease of adoption, and efficient use of resources. Finally, we discuss the research challenges that lie ahead in this space.
AB - This paper discusses the challenges in providing AI functionality “as a Service” (AIaaS) in enterprise contexts, and proposes solutions to some of these challenges. The solutions are based on our experience in designing, deploying, and testing AI services with a number of customers of ServiceNow, an Application Platform as a Service that enables digital workflows and simplifies the complexity of work in a single cloud platform. Some of the underlying ideas were developed when many of the authors were part of DxContinuum inc, a machine learning (ML) startup that ServiceNow bought in 2017 with the express purpose of embedding ML in the ServiceNow platform. The widespread adoption of ServiceNow by the majority of large corporations has given us the opportunity to interact with customers in different markets and to appreciate the needs, fears and barriers towards adopting AIaaS and to design solutions that respond to such barriers. In this paper we share the lessons we learned from these interactions and present the resulting framework and architecture we adopted, which aims at addressing fundamental concerns that are sometimes conflicting with each other, from automation to security, performance, effectiveness, ease of adoption, and efficient use of resources. Finally, we discuss the research challenges that lie ahead in this space.
KW - Cloud computing
KW - Machine learning
KW - PaaS
UR - http://www.scopus.com/inward/record.url?scp=85076343328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076343328&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33702-5_25
DO - 10.1007/978-3-030-33702-5_25
M3 - Conference contribution
AN - SCOPUS:85076343328
SN - 9783030337018
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 331
EP - 344
BT - Service-Oriented Computing - 17th International Conference, ICSOC 2019, Proceedings
A2 - Yangui, Sami
A2 - Drira, Khalil
A2 - Bouassida Rodriguez, Ismael
A2 - Tari, Zahir
PB - Springer Paris
T2 - 17th International Conference on Service-Oriented Computing, ICSOC 2019
Y2 - 28 October 2019 through 31 October 2019
ER -