Amazon SageMaker AI vs. AWS Fargate

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

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Fargate is well suited for web APIs, microservices, background jobs, and applications with changing traffic. It is useful when teams want to run containers without managing servers. It is less appropriate for workloads that run constantly at large scale, need special hardware, require deep server control, or depend on very low costs and highly customized networking or storage requirements.

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• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

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

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• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

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• Offers less control over the underlying server.
• Storage options are more limited than EC2.
• Large numbers of tasks may hit launch-rate limits.

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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 !

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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.

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Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

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

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Pricing and billing of AWS Fargate is loosely tied to your exisiting AWS billing. You're unlikely to only use Fargate in your AWS subscription, so you get billed for everything alltoghter.

Fargate is naturally a bit more expensive that usuel docker services, but with careful planning and architecturing, you can have a very manageable cost.

You can also rely on Saving plans to reduce your bill.

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• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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• Has made seasonal apps/tools cheap to run/maintain.
• Pay for what you use helps compare many A/B tests.
• No server management means faster deploys.

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