Amazon S3 (Simple Storage Service) vs. Azure Machine Learning

Amazon S3 is a great service to safely backup your data where redundancy is guaranteed and the cost is fair. We use Amazon S3 for data that we backup and hope we never need to access but in the case of a catastrophic or even small slip of the finger with the delete command we know our data and our client's data is safely backed up by Amazon S3. Transferring data into Amazon S3 is free but transferring data out has an associated, albeit low, cost per GB. This needs to be kept in mind if you plan on transferring out a lot of data frequently. There may be other cost effective options although Amazon S3 prices are really low per GB. Transferring 150TB would cost approximately $50 per month.

Incentivized

For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.

All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.

If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.

• Fantastic developer API, including AWS command line and library utilities.
• Strong integration with the AWS ecosystem, especially with regards to access permissions.
• It's astoundingly stable- you can trust it'll stay online and available for anywhere in the world.
• Its static website hosting feature is a hidden gem-- it provides perhaps the cheapest, most stable, most high-performing static web hosting available in PaaS.

Incentivized

• User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
• Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
• Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
• Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
• Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

Incentivized

• Web console can be very confusing and challenging to use, especially for new users
• Bucket policies are very flexible, but the composability of the security rules can be very confusing to get right, often leading to security rules in use on buckets other than what you believe they are

Incentivized

• It would be great to have text tips that could ease new users to the platform, especially if an error shows up
• Scenario-based documentation
• Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

Incentivized

Due to princing, availability and scalability.

Incentivized

It is tricky to get it all set up correctly with policies and getting the IAM settings right. There is also a lot of lifecycle config you can do in terms of moving data to cold/glacier storage. It is also not to be confused with being a OneDrive or SharePoint replacement, they each have their own place in our environment, and S3 is used more by the IT team and accessed by our PHP applications. It is not necessarily used by an average everyday user for storing their pictures or documents, etc.

Incentivized

Easy and fastest way to develop, test, deploy and monitor the machine learning model.

- Easy to load the data set

-Drag and drop the process of the Machine learning life cycle.

AWS has always been quick to resolve any support ticket raised. S3 is no exception. We have only ever used it once to get a clarification regarding the costs involved when data is transferred between S3 and other AWS services or the public internet. We got a response from AWS support team within a day.

Incentivized

Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.

Incentivized

Not sure

Overall, we found that Amazon S3 provided a lot of backend features Google Cloud Storage (GCS) simply couldn't compare to. GCS was way more expensive and really did not live up to it. In terms of setup, Google Cloud Storage may have Amazon S3 beat, however, as it is more of a pseudo advanced version of Google Drive, that was not a hard feat for it to achieve. Overall, evaluating GCS, in comparison to S3, was an utter disappointment.

Incentivized

It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.

Incentivized

• It practically eliminated some real heavy storage servers from our premises and reduced maintenance cost.
• The excellent durability and reliability make sure the return of money you invested in.
• If the objects which are not active or stale, one needs to remove them. Those objects keep adding cost to each billing cycle. If you are handling a really big infrastructure, sometimes this creates quite a huge bill for preserving un-necessary objects/documents.

Incentivized

• Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
• Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
• Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

Incentivized