🐾SageMaker and beyond: Exploring options for deploying ML models on AWS🐾
🤓Ever found yourself in the labyrinth of model deployment options and wondered, "Why does everyone use SageMaker endpoints for model deployment?" 🤔 Let's unravel the mystery behind that and explore alternatives like EC2, ECS/EKS, and Lambda to help you make an informed decision.
🤖 SageMaker Endpoint
The preferred option for production workloads. It is easy to deploy, has blue-green deployment and monitoring solution out of the box. It's the go-to choice when you need reliability and efficiency and don’t want to manage underlying resources. Despite the fact that AWS manages everything for you, SageMaker endpoints provide you with flexibility of choice between endpoint types and instance types.
🤖 Lambda
Suitable for hosting simple models with sporadic load as you pay only for the time Lambda actually runs. Keep in mind the memory limit of 10,240 MB and a 15-minute execution limit, as well as cold starts. You will need to create a custom image, but it's a small price for the advantages Lambda brings. You won’t be able to use GPU for your model.
🤖 ECS/EKS
It is a great option when you do not want to be vendor-locked or are thinking of migrating to another cloud. Also, you can control all settings and customize your model serving in any way. To start working, you should develop a container for ML model serving. It’s possible to use GPU and run Deep Learning models with ECS and EKS.
🤖 EC2
The most customizable option, but it requires you to manage underlying resources. You can use Deep Learning AMIs provided by AWS or develop your own. It can be not so cheap because you will pay for the time instance is running, even if your model failed to initialize. Monitoring of the model should be set up separately using CloudWatch Agent and custom CloudWatch metrics.
Each option has its own flavor, and the choice depends on your specific needs. SageMaker brings a lot of out-of-the-box options, Lambda offers low cost for models with unpredictable load, ECS/EKS manages the environment for running containers for you, and EC2 allows you to bring any customization. The playground is vast; choose your ML deployment option wisely! 🚀🧠
Related posts you may be interested in:
- SageMaker Real-time Endpoints types
- Lambda development - Custom base image
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