Since 2017, Amazon SageMaker has empowered organizations to harness machine learning for diverse applications. Initially a tool for data scientists, its utility has expanded to include MLOps engineers, data engineers and business stakeholders.
The SageMaker AI rebrand underscores its evolution into a comprehensive platform integrating data management and AI development.
“A few years ago, machine learning was mostly a data scientist’s pursuit, and data scientists were taking data within organizations and building machine learning models,” said Ankur Mehrotra (pictured), director and general manager of Amazon SageMaker at Amazon Web Services Inc. “Over the years, we saw more personas getting involved. We saw MLOps engineers getting involved to put those models in production. We then saw data engineers get involved to help data scientists prepare data to build these models. Then we saw business stakeholders involved in the decision-making process, etc.”
Mehrotra spoke with theCUBE Research’s Dave Vellante and John Furrier for theCUBE’s “Cloud AWS re:Invent Coverage,”during an …