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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    TensorFlow

    Score7.6 out of 10
    N/ATensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A

    Visual Studio Test Professional

    Score7 out of 10
    N/AAn add-on for the Visual Studio IDE, Visual Studio Test Professional subscription helps teams drive quality and speed. It includes test case management and collaboration features that streamline quality control and support continuous delivery.

    $2,169

    for the first year (renews at $869)

    Pricing
    TensorFlowVisual Studio Test Professional
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    TensorFlowVisual Studio Test Professional
    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
    TensorFlowVisual Studio Test Professional
    Considered Both Products
    Open Source
    No answer on this topic
    Microsoft
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    100%
    Would buy again
    12 Answers
    Delivers good value for the price
    No answers on this topic
    91%
    Delivers good value for the price
    10 Answers
    Happy with the feature set
    No answers on this topic
    100%
    Happy with the feature set
    12 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    9 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    12 Answers
    Best Alternatives
    TensorFlowVisual Studio Test Professional
    Small Businesses
    Google Cloud AI
    Score8.7 out of 10
    No answers on this topic
    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    No answers on this topic
    Enterprises
    Google Cloud AI
    Score8.7 out of 10
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    All AlternativesView all alternativesView all alternatives
    User Ratings
    TensorFlowVisual Studio Test Professional
    Likelihood to Recommend
    6.0
    (15 ratings)
    7.0
    (15 ratings)
    Usability
    9.0
    (1 ratings)
    7.0
    (10 ratings)
    Support Rating
    9.1
    (2 ratings)
    8.5
    (10 ratings)
    Implementation Rating
    8.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    TensorFlowVisual Studio Test Professional
    Likelihood to Recommend
    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).
    Incentivized
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    Microsoft
    It would be well suited if we used it with Azure DevOps as we can effortlessly integrate the test cases and even stories or tasks to stay on track with our work. Those test cases can even be reused across multiple projects. Using any other third-party tools, such as Jira, can be less appropriate, as it's not a Microsoft tool, and its capabilities will be limited.
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    Pros
    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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    Microsoft
    • Availability of the desktop client or the web interface. The web interface being the favorite and providing a better experience.
    • It enables you to write unit tests with so much ease.
    • Allows the recording and repeating of manual tests
    • It can be set up for collaboration.
    Incentivized
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    Cons
    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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    Microsoft
    • The user community of the Visual Studio Test product is weak. For instant problems with this product, it is necessary to quickly reach the source of the error.
    • Licence fees need to be more reasonable. License prices need to be reduced so that they can easily compete with free testing tools.
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    Usability
    Open Source
    Support of multiple components and ease of development.
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    Microsoft
    It is very usable if you are familiar with Visual Studio to begin with. If you are new to the interface, it can be a long ramp up period for Testers not used to the GUI. There is always the web option which seems to be more intuitive for many Testers.
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    Support Rating
    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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    Microsoft
    Visual Studio Test Professional is backed up by the full support of the Microsoft Corporation. That means twenty-four/seven customer support by quality, highly-trained professionals who understand every possible issue that you have experienced before. They are nice, efficient, and highly professional. I recommend them.
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    Implementation Rating
    Open Source
    Use of cloud for better execution power is recommended.
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    Microsoft
    No answers on this topic
    Alternatives Considered
    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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    Microsoft
    The visual Studio Test tool is faster than other tools. Since the development and testing processes are in one tool, it is more profitable in terms of cost. It is more inconvenient to write a test case in DevOps.
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    Return on Investment
    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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    Microsoft
    • One of the positive ROIs of Visual Studios is the fact that it makes producing our work at a quick rate, things like Intellisense make our work get produced at a much higher rate which is good for our return of investment.
    • Testing by the developers has increased by 23%, we now take the time to actually test our product before we send it to our QA people.
    • I am not aware of any negative ROI aspects to our company that have been found.
    Incentivized
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