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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Microsoft Azure
Score 8.6 out of 10
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Microsoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.
$29
per month
Pricing
Amazon Deep Learning AMIs
Microsoft Azure
Editions & Modules
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Developer
$29
per month
Standard
$100
per month
Professional Direct
$1000
per month
Basic
Free
per month
Offerings
Pricing Offerings
Amazon Deep Learning AMIs
Microsoft Azure
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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The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
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.
In terms of cloud computing, Microsoft Azure is the only comprehensive result the company offers. Regardless of how big or small an organization is, it can make use of this system. As a cyber-security professional, this is your best option for data management. A business that wants to minimize capital expenditures can use Microsoft Azure. Many Microsoft services accept it. People with little or no knowledge of cloud computing may find it impossible. It isn’t the solution for companies that don’t want to risk having only one platform and infrastructure vendor.
Azure simply provides end to end life cycle. Starting from the development to automated deployment, you will find [a] bunch of options. Custom hook-points allow [integration] on-premise resources as well.
Excellent documentation around all the services make it really easy for any novice. Overall support by [the] community and Azure Technical team is exceptional.
BOT Services, Computer Vision services, ML frameworks provide excellent results as compare to similar services provided by other giants in the same space.
Azure data services provide excellent support to ingest data from different sources, ETL, and consumption of data for BI purpose.
In our experience, Azure Kubernetes Survice was difficult to set up, which is why we used Kubernetes on top of VMs.
Azure REST API is a bit difficult to use, which made it difficult for us to automate our interactions with Azure.
Azure's Web UI does a good job of showing metrics on individual VMs, but it would be great if there was a way to show certain metrics from multiple VMs on one dashboard. For example, hard drive usage on our database VMs.
Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.
Microsoft Azure's overall usability has been better than expected. Often times vendors promise the world, only to leave you with a run-down town. Not the case with our experience. From an implementation perspective, all went perfect, and from the user-facing experience we have had no technical issues, just some learning curve issues that are more about "why" than "how"
Support is easy with all the knowledge base articles available for free on the web. Plus, if you have a preferred status you can leverage their concierge support to get rapid response. Sometimes they’ll bounce you around a lot to get you to the right person, but they are quite responsive (especially when you are paying for the service). Many of the older Microsoft skills are also transferable from old-school on-prem to Azure-based virtual interfaces.
As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.
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.
As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.
