Amazon Deep Learning AMIs vs. Google Assistant vs. Microsoft Azure

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.

Incentivized

I'm in a Me vs. The World environment rather often. I can connect to my outer realm when heading to live meetings. Auditions, job assignments all via my assistant. I like having the ability to capture the moment and rewrite it as well. This is a primary driver for me. Sometimes branching out or when collaborating, I think I work a little harder in the moment than Google Assistant might but that is moreso my limitations and not the feature so much. I catch this scene when I'm in a group environment or at times having to create and respond to a larger scale event. Not a deal breaker for me however.

Incentivized

Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.

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

Incentivized

• To-do lists and task boards so I can work on it better, and can ask quickly on what I need to do.
• Saves time and increases efficiency - I can ask and can answer relevant answers
• Set-up meetings - quickly scheduling and checking for time which suits all people

Incentivized

• Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
• You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
• The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
• The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.

Incentivized

• 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

Incentivized

• I think newer, complementary ideas are a bit sharper than Google Assistant especially in a Q&A environment or when seeking some depth to a subject. That enhancement is to be expected I feel. And Google Assistant is not so self limiting so I don't have a lot of improvement needs because I use this for what I've become accustomed to and for the ability overall.
• It is always important to do your best around hectic places, in bad tower signal areas or even if trying to do something new while using Google Assistant. Have patience in the setting. It pays off.

Incentivized

• The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
• The documentation needs to be improved, and some advanced configuration options require research and experimentation.
• Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.

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.

I feel this can be adjusted and after some trial and error you sort of start knowing what will work and how. And I have to say the overall impact becomes personal and we are all different. I'm small scale and as I've said, it works.

Incentivized

As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.

Incentivized

It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators

Incentivized

We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!

Incentivized

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.

Incentivized

I chose this because it was easier for me and can be accessed via mobile and laptop too because it enables cross device support because it helps in adding more depth to my life, and can help me save tons of time.

Incentivized

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.

Incentivized

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

Incentivized

• positive because it saves my time and improves productivity
• I can do quick research based on my thoughts and even asking it to write notes

Incentivized

• For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
• DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
• Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.

Incentivized