Azure Machine Learning vs. TensorFlow

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
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
TensorFlow
Score 9.0 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
Pricing
Azure Machine LearningTensorFlow
Editions & Modules
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
No answers on this topic
Offerings
Pricing Offerings
Azure Machine LearningTensorFlow
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
Azure Machine LearningTensorFlow
Considered Both Products
Azure Machine Learning

No answer on this topic

TensorFlow
Chose TensorFlow
Most of the machine learning platforms these days support integration with R and Python libraries. So, the use of reusable libraries is not an issue. TensorFlow performs well in cloud hosting and support for GPU/TPU. However, where it lacks compared to Azure is a graphical …
Top Pros
Top Cons
Best Alternatives
Azure Machine LearningTensorFlow
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
Azure Machine LearningTensorFlow
Likelihood to Recommend
8.0
(4 ratings)
8.5
(14 ratings)
Likelihood to Renew
7.0
(1 ratings)
-
(0 ratings)
Usability
7.0
(2 ratings)
9.0
(1 ratings)
Support Rating
7.9
(2 ratings)
9.1
(2 ratings)
Implementation Rating
8.0
(1 ratings)
8.0
(1 ratings)
User Testimonials
Azure Machine LearningTensorFlow
Likelihood to Recommend
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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Open Source
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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Pros
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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Open Source
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
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Cons
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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Open Source
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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Usability
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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Open Source
Support of multiple components and ease of development.
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Support Rating
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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Open Source
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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Implementation Rating
Microsoft
Not sure
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Open Source
Use of cloud for better execution power is recommended.
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Alternatives Considered
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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Open Source
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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Return on Investment
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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Open Source
  • Learning is s bit difficult takes lot of time.
  • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
  • Once you have learned this, it make your job very easy of getting the good result.
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