R E S E A R C H N O T E
P R O G R A M : A N A L Y T I C S
D O C U M E N T N U M B E R : T 1 7 6 D E C E M B E R 2 0 1 9
©2019 Nucleus Research Inc. | 100 State Street, Boston, MA, 02109 | +1 (617) 720-2000 | NucleusResearch.com
1
A M A Z O N S A G E M A K E R
E N A B L E S M L S A V I N G S
A N A L Y S T
Daniel Elman
T H E B O T T O M L I N E
At this stage, the viability and effectiveness of machine learning for tasks like classification,
regression, and image recognition is well-documented across industry and academia. As
more organizations look to leverage these capabilities, a primary challenge is managing the
data, models, and infrastructure in an efficient and agile manner. Since these businesses
have migrated workloads to the cloud, cloud vendors like AWS recognized the opportunity
to deliver additional value in the form of managed machine learning (ML) services. In 2017,
AWS announced Amazon SageMaker, a fully managed service for the creation, training, and
deployment of machine learning models. In examining companies who have deployed
SageMaker, Nucleus has found the key benefits include an accelerated development cycle,
cost savings, increased developer productivity, and increased machine learning agility.
