Amazon Deep Learning AMIs vs. wasmCloud

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

If you want to start pivoting your legacy code into a serverless paradigm this is the platform to target. If you want to start moving away from walled gardens and back into a space where code isn't centralized, costs can be offset by utilizing customer or edge hardware this is the platform to focus on. If you want a model that will scale well past many of the existing serverless frameworks, that won't get caught up in the security weaknesses of current paradigms this is what you need to invest in. If you want modern code to interact with legacy code in a safe way that doesn't expose vulnerabilities you can't patch against wasmCloud is awesome. If you want to write simple code that can jump from cloud to cloud to the user or edge node check it out!

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

Incentivized

• Host density of workloads
• Streamlined security approach to writing code and reducing bloat
• Interoperability of modules written in different languages

• 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

• Simplified open-source model for on-boarding devs, i.e. bare host no elixir/GUI
• Home-labbers guide to wasmCloud
• Spark more community involvement for capability providers

wasmCloud is still in the early stages of development but the product is rapidly evolving and stabilizing and I feel it will be a preferred way of deploying and managing remote, distributed applications in the future.

Incentivized

The core team is extremely responsive in their provided channels. From slack to github issues, there is never a long wait for feedback or guidance.

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

Have not made a purchase yet as we are still in the POC stage.

Incentivized

The team is incredibly helpful and informative about the product - I have not needed to use their professional services thus far.

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

Incentivized

• WasmCloud has massively simplified the required ops expertise needed to run modern software.