Amazon Deep Learning AMIs vs. AWS Fargate

Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle. If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.

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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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• Setting up environment
• Support for different types of machines
• Perfect for Machine Learning / Deep Learning use cases
• Nvidia / Cuda / Conda support easily

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

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• Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
• It can be a bit confusing to switch between frameworks for novice users

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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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Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.

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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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• Saves a lot of Infra Costs
• Saves a lot of time in handling environment issues
• Easy to start a new instance

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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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