Amazon Tensor Flow vs. Azure Machine Learning

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Tensor Flow
Score 8.0 out of 10
N/A
Amazon TensorFlow enables developers to quickly and easily get started with deep learning in the cloud.N/A
Azure Machine Learning
Score 7.9 out of 10
N/A
Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
Pricing
Amazon Tensor FlowAzure Machine Learning
Editions & Modules
No answers on this topic
Studio Pricing - Free
$0.00
per month
Production Web API - Dev/Test
$0.00
per month
Studio Pricing - Standard
$9.99
per ML studio workspace/per month
Production Web API - Standard S1
$100.13
per month
Production Web API - Standard S2
$1000.06
per month
Production Web API - Standard S3
$9999.98
per month
Offerings
Pricing Offerings
Amazon Tensor FlowAzure Machine Learning
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Amazon Tensor FlowAzure Machine Learning
Considered Both Products
Amazon Tensor Flow
Chose Amazon Tensor Flow
Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking.

AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities
Azure Machine Learning

No answer on this topic

Top Pros
Top Cons
Best Alternatives
Amazon Tensor FlowAzure Machine Learning
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Posit
Posit
Score 9.1 out of 10
Posit
Posit
Score 9.1 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon Tensor FlowAzure Machine Learning
Likelihood to Recommend
9.0
(1 ratings)
8.0
(4 ratings)
Likelihood to Renew
-
(0 ratings)
7.0
(1 ratings)
Usability
-
(0 ratings)
7.0
(2 ratings)
Support Rating
-
(0 ratings)
7.9
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Amazon Tensor FlowAzure Machine Learning
Likelihood to Recommend
Amazon AWS
A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.
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Microsoft
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.
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Pros
Amazon AWS
  • Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
  • Tensor Flow allows us to train our models much faster than in our on-premise equipment.
  • Most of the pre-trained models are easy to adapt to our clients' needs.
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Microsoft
  • 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.
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Cons
Amazon AWS
  • SageMaker isn't available in all regions. This is complicated for some clients overseas.
  • For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
  • Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.
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Microsoft
  • 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
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Usability
Amazon AWS
No answers on this topic
Microsoft
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.
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Support Rating
Amazon AWS
No answers on this topic
Microsoft
Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.
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Implementation Rating
Amazon AWS
No answers on this topic
Microsoft
Not sure
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Alternatives Considered
Amazon AWS
Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.
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Microsoft
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.
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Return on Investment
Amazon AWS
  • Positive: It has allowed us to work with our overseas teams without any large hardware investing.
  • Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
  • Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.
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Microsoft
  • 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
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ScreenShots