Kumar Venkateswar, Amazon
In this tutorial, we'll talk about how Amazon Web Services customers are using Amazon SageMaker, a fully-managed service for machine learning, to accelerate the time-to-production for their ML models. Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the algorithm, tune and optimize it for deployment, make predictions, and take action.
Takeaways:
There are multiple aspects to operationalizing ML, including security and standardized environments for data exploration and ingestion, scaling training from small datasets to large datasets, readying models for deployment, and monitoring/managing production deployments. We'll talk about how SageMaker helps with each of these stages.
Prerequisites:
Understanding of the full ML workflow, but otherwise none.
Kumar Venkateswar, Amazon
Kumar Venkateswar currently leads the Amazon SageMaker product management team. He came to Amazon to improve the machine learning platform through launching Amazon SageMaker, the Amazon Deep Learning AMI, and several features in Amazon Machine Learning which provided a better user experience for data scientists in AWS. He has over a decade of experience working in product management in cloud services, including on machine learning, search, storage, and high availability features in petabyte-scale environments. He is an alumnus of the University of Chicago Booth School of Business (General Management Program) and the University of Illinois at Urbana-Champaign (MS, BS Electrical Engineering).
author = {Kumar Venkateswar},
title = {Using Amazon {SageMaker} to Operationalize Machine Learning},
year = {2019},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = may
}