INSUBCONTINENT EXCLUSIVE:
As part of the flurry of announcements coming this week out of AWS re:Invent, Amazon announced the release of Amazon SageMaker Operators for
Kubernetes, a way for data scientists and developers to simplify training, tuning and deploying containerized machine learning
models.
Packaging machine learning models in containers can help put them to work inside organizations faster, but getting there often
requires a lot of extra management to make it all work
Amazon SageMaker Operators for Kubernetes is supposed to make it easier to run and manage those containers, the underlying infrastructure
needed to run the models and the workflows associated with all of it.
&While Kubernetes gives customers control and portability, running ML
workloads on a Kubernetes cluster brings unique challenges
For example, the underlying infrastructure requires additional management such as optimizing for utilization, cost and performance;
complying with appropriate security and regulatory requirements; and ensuring high availability and reliability,& AWS& Aditya Bindal wrote
in a blog post introducing the new feature.
When you combine that with the workflows associated with delivering a machine learning model
inside an organization at scale, it becomes part of a much bigger delivery pipeline, one that is challenging to manage across departments
and a variety of resource requirements.
This is precisely what Amazon SageMaker Operators for Kubernetes has been designed to help DevOps
&Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon
SageMaker and Kubernetes workflows
Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it
easier to build, train, and deploy machine learning (ML) models at scale,& Bindal wrote.
The promise of Kubernetes is that it can
orchestrate the delivery of containers at the right moment, but if you haven&t automated delivery of the underlying infrastructure, you can
over (or under) provision and not provide the correct amount of resources required to run the job
That where this new tool, combined with SageMaker, can help.
&With workflows in Amazon SageMaker, compute resources are pre-configured and
optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near 100%
utilization,& Bindal wrote.
Amazon SageMaker Operators for Kubernetes are available today in select AWS regions.
AWS releases SageMaker to
make it easier to build and deploy machine learning models
