英文:
AWS Sagemaker vs ECS for model hosting
问题
我有预训练的模型文件存储在S3存储桶中。我想创建一个服务，加载这个模型并用于推断。
我正在AWS生态系统中工作，对于模型部署，我在使用ECS和Sagemaker之间感到困惑。选择其中之一的一些优缺点是什么？
英文:
I have pretrained model artifacts stored in S3 buckets. I want to create a service that loads this model and uses it for inference.
I am working in AWS ecosystem and confused between using ECS vs Sagemaker for model deployment?
What are some pros/cons for choosing one over other?
答案1
得分: 11
SageMaker有更高的价格标签，但它承担了部署机器学习模型的许多重要工作，例如连接各个部分（负载均衡器、gunicorn、CloudWatch、自动缩放...），并且更容易自动化流程，如A/B测试。
如果你有一支强大的DevOps团队，没有更重要的事情要做，你可以构建一个比SageMaker选项更便宜的流程。ECS和EKS同时也在大力工作，以使自动化机器学习模型部署变得非常容易。然而，它们始终更加通用，而SageMaker专注于机器学习，因此对于这些用例来说更容易。
在使用云的一般模式是，在想要快速迭代且不知道未来问题出现在哪里时，早期使用托管服务。一旦系统不断增长，你开始在各个方面感到一些困难，就可以决定花时间改进系统的某个部分。因此，如果你不知道利弊，从使用更简单的选项开始。
英文:
SageMaker has a higher price mark but it is taking a lot of the heavy lifting of deploying a machine learning model, such as wiring the pieces (load balancer, gunicorn, CloudWatch, Auto-Scaling...) and it is easier to automate the processes such as A/B testing.
If you have a strong team of DevOps that have nothing more important to do, you can build a flow that will be cheaper than the SageMaker option. ECS and EKS are doing at the same time a lot of work to make it very easy for you to automate the machine learning model deployments. However, they will always be more general purpose and SageMaker with its focus on machine learning will be easier for these use cases.
The usual pattern of using the cloud is to use the managed services early on as you want to move fast and you don't really know where are your future problems. Once the system is growing and you start feeling some pains here and there, you can decide to spend the time and improve that part of the system. Therefore, if you don't know the pros/cons, start with using the simpler options.
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