AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed with deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new…
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Amazon Elastic Compute Cloud (EC2)
Score 8.8 out of 10
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Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. Users can launch instances with a variety of OSs, load them with custom application environments, manage network access permissions, and run images on multiple systems.
$0.01
per IP address with a running instance per hour on a pro rata basis
Pricing
Amazon Deep Learning AMIs
Amazon Elastic Compute Cloud (EC2)
Editions & Modules
No answers on this topic
Data Transfer
$0.00 - $0.09
per GB
On-Demand
$0.0042 - $6.528
per Hour
EBS-Optimized Instances
$0.005
per IP address with a running instance per hour on a pro rata basis
Carrier IP Addresses
$0.005 - $0.10
T4g Instances
$0.04
per vCPU-Hour Linux, RHEL, & SLES
T2, T3 Instances
$0.05 ($0.096)
per vCPU-Hour Linux, RHEL, & SLES (Windows)
Offerings
Pricing Offerings
Amazon Deep Learning AMIs
Amazon Elastic Compute Cloud (EC2)
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon Deep Learning AMIs
Amazon Elastic Compute Cloud (EC2)
Features
Amazon Deep Learning AMIs
Amazon Elastic Compute Cloud (EC2)
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
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.
Suitable for companies that are looking for performance at a competitive price, flexibility to switch instance type even with RI, flexibility to add-on IOPS, option to lower running cost with the regular introduction of new instance type that comes with higher performance but at a lower cost.
The choices on AMIs, instance types and additional configuration can be overwhelming for any non-DevOps person.
The pricing information should be more clear (than only providing the hourly cost) when launching the instance. AWS DynamoDB gives an estimated monthly cost when creating tables, and I would love to see similar cost estimation showing on EC2 instances individually, as not all developers gets access to the actual bills.
The term for reserving instances are at least 12 months. With instance types changing so fast and better instances coming out every other day, it's really hard to commit to an existing instance type for 1 or more years at a time.
You an start using EC2 instances immediately, is so easy and intuitive to start using them, EC2 has wizard to create the EC2 instances in the web browser or if you are code savvy you can create them with simple line in the CLI or using an SDK. Once you are comfortable using EC2, you can even automate the process.
AWS's support is good overall. Not outstanding, but better than average. We have had very little reason to engage with AWS support but in our limited experience, the staff has been knowledgeable, timely and helpful. The only negative is actually initiating a service request can be a bit of a pain.
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.
Amazon EC2 is super flexible compared to the PaaS offerings like Heroku Platform and Google App Engine since with Amazon EC2, we have access to the terminal. In terms of pricing, it's basically just the same as Google Compute Engine. The deciding factor is Amazon EC2's native integration with other AWS services since they're all in the same cloud platform.
It reduced the need for heavy on-premises instances. Also, it completely eliminates maintenance of the machine. Their SLA criteria are also matching business needs. Overall IAAS is the best option when information is not so crucial to post on the cloud.
It makes both horizontal and vertical scaling really easy. This keeps your infrastructure up and running even while you are increasing the capacity or facing more traffic. This leads to having better customer satisfaction.
If you do not choose your instance type suitable for your business, it may incur lots of extra costs.
