TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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Visual Studio Test Professional
Score 7.0 out of 10
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An 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.
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).
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.
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.
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.
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.
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.
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.
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
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.
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.
