Amazon Deep Learning AMIs vs. Google Cloud Virtual Private Cloud (VPC)

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

An effective pricing strategy is in place. Google Cloud VPC is the most secure since it runs on a private network and never contacts the public network. Google is well-known for its AI/ML and Kubernetes engines, both of which have a leg up on the competition. Google Cloud VPC's database services are yet to be improved.

Incentivized

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

Incentivized

• Can utilize one VPC to connect many regions without using the internet.
• Unlike competitors, VPC can automatically build a virtual topology.
• Configure private communication inside companies.

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

• Sometimes the group settings tab lags a little
• Although overall it's easy to use, some of the sections lack documentation
• Routes section is very poorly implemented, requires a lot of digging to learn how to use

Incentivized

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

Google VPC and networking infrastructure is very matured and is built later after Amazon VPC. It made sure to address all the limitations faced by amazon VPC. Google Virtual private cloud is across regions while amazon VPC covers only one region but multiple zones. Google VPC is a global resource while AWS is a regional resource.

Incentivized

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

Incentivized

• Using Google Cloud VPC cloud storage has saved us on infra budget and per TB storage cost
• Using MySQL databases on Google Cloud VPC has improved the performance and cost of operation and maintenance
• Better pricing model has improvement on operational cost and increased revenue

Incentivized