Amazon SageMaker vs. AWS Fargate

Amazon Sagemaker suits well in areas of data science and Machine learnings where medium to high-volume data is to be used for analysis. For a lean and platform agnostic deployment, it provides kubernetes integration to containerize the solution and deploy on any platform. It is one of the best solution for technical users for training Machine Learning models.

Incentivized

If you need to deploy Docker containers, Amazon Fargate is a very good fit. It integrates very well with other AWS services like RDS, EFS, and Secrets manager. You can have a very robust application using those services. In case you have many containers to deploy, it is however more expensive
that if you use other services like ECS or EKS, since they allow you to
share the same infrastructure to deploy multiple containers.

Incentivized

• SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
• SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
• SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

Incentivized

• scalability
• ease of use
• agility to up or downsize

Incentivized

• Searching and descriptions can be easier to read and interpret.
• Training modules and customer service training representative could make on boarding employees easier.

Incentivized

• can't think of any

Incentivized

It's a very practical service to use. If you need to deploy any application with a Database, disk storage, you're pretty much set.

Everything around that can be taken care of using other AWS services. Like secrets manager, certificate manager, RDS ...

And the CI/CD part is also very easy to setup, you only need on AWS CLI command to trigger a deployment, and done !

Incentivized

AWS provides different support tiers. They are usually very reactive and are able to help solve the issues very quickly.

As for everything, the higher the support tier you get, the better and faster support you get.

If you're also a part of big company, you probably have solution architects at your disposal to help you with any inqueries.

Incentivized

We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.

Incentivized

We found the extra cost saved us frustration and time and ultimately money in the long run

Incentivized

• Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
• It also saves your money from investing in a physical server for very rare use.
• However, the pricing is high, but it will cost you only for what you use.

Incentivized

• easier to optimize our computer costs
• transition from server to serverless was easier once we decided to adopt Fargate

Incentivized