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 Web Services
Score 8.7 out of 10
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Amazon Web Services (AWS) is a subsidiary of Amazon that provides on-demand cloud computing services. With over 165 services offered, AWS services can provide users with a comprehensive suite of infrastructure and computing building blocks and tools.
$100
per month
Pricing
Amazon Deep Learning AMIs
Amazon Web Services
Editions & Modules
No answers on this topic
Free Tier
$0
per month
Basic Environment
$100 - $200
per month
Intermediate Environment
$250 - $600
per month
Advanced Environment
$600-$2500
per month
Offerings
Pricing Offerings
Amazon Deep Learning AMIs
Amazon Web Services
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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AWS allows a “save when you commit” option that offers lower prices when you sign up for a 1- or 3- year term that includes an AWS service or category of services.
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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If there is one thing I think AWS needs improvement on, it is the administration dashboard. It can be a nightmare to use especially when trying to access billing. This could be made better, honestly, as there should be a simplified way to access simple admin features.
While AWS was fairly easy to integrate into our solutions, it is not as easy to use without some IT knowledge. The dashboards are complicated and designed for someone who is computer savvy. If you are just want to keep track of billing, for example, you may need to take a course or spend a few hours with someone being walked through the admin console.
AWS does tend to be slow at times. If you do not have a fast internet connection, it can take time to access services that are hosted on AWS. This is not always the case but we have had clients complain about this if they are trying to access a service from multiple points (IP addresses). The only real fix we found was to make our files cache to another server and only keep current data accessible to clients.
We are almost entirely satisfied with the service. In order to move off it, we'd have to build for ourselves many of the services that AWS provides and the cost would be prohibitive. Although there are cost savings and security benefits to returning to the colo facility, we could never afford to do it, and we'd hate to give up the innovation and constant cycle of new features that AWS gives us.
Our cloud platform architecture was designed in order to collect, analyze, and optimize modern networks, from AWS-powered computing, networking, storage, and more. So, we needed a reliable, scalable, and secure global computing infrastructure. Auto Scaling and Elastic Load Balancing were key features in our evaluation and later on for scalability and high performance. We being a cybersecurity company, we needed to ensure that our cloud provider utilizes an end-to-end approach to secure and harden the infrastructure, including physical, operational, and software measures - which AWS had all in place.
AWS does not provide the raw performance that you can get by building your own custom infrastructure. However, it is often the case that the benefits of specialized, high-performance hardware do not necessarily outweigh the significant extra cost and risk. Performance as perceived by the user is very different from raw throughput.
The customer support of Amazon Web Services are quick in their responses. I appreciate its entire team, which works amazingly, and provides professional support. AWS is a great tool, indeed, to provide customers a suitable way to immediately search for their compatible software's and also to guide them in a good direction. Moreover, this product is a good suggestion for every type of company because of its affordability and ease of use.
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 Web Services is well suited when we have a huge amount of data to store, process, manipulate and get meaningful information out of. It is also suitable when we need very fast data retrieval from the database. They provide a superior product at a fair price which allows us to further our goals and push the limits of what we are capable of as a team / company.
