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9 Ratings
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Score 9.5 out of 100
46 Ratings
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Score 8 out of 100

Attribute Ratings

  • IBM Watson Machine Learning is rated higher in 1 area: Likelihood to Recommend
  • TensorFlow is rated higher in 1 area: Support Rating

Likelihood to Recommend

10.0

IBM Watson Machine Learning

100%
2 Ratings
7.4

TensorFlow

74%
14 Ratings

Usability

IBM Watson Machine Learning

N/A
0 Ratings
9.0

TensorFlow

90%
1 Rating

Support Rating

4.0

IBM Watson Machine Learning

40%
2 Ratings
9.1

TensorFlow

91%
4 Ratings

Implementation Rating

IBM Watson Machine Learning

N/A
0 Ratings
8.0

TensorFlow

80%
2 Ratings

Likelihood to Recommend

IBM

IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.
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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

IBM

  • Good machine learning tool
  • Easy integration
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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

IBM

  • Proper usage of REST API documentation is missing.
  • Not localization friendly, cannot support regional or local language documents.
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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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Pricing Details

IBM Watson Machine Learning

Starting Price

Editions & Modules

IBM Watson Machine Learning editions and modules pricing
EditionModules

Footnotes

    Offerings

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services

    Entry-level set up fee?

    No setup fee

    Additional Details

    TensorFlow

    Starting Price

    Editions & Modules

    TensorFlow editions and modules pricing
    EditionModules

    Footnotes

      Offerings

      Free Trial
      Free/Freemium Version
      Premium Consulting/Integration Services

      Entry-level set up fee?

      No setup fee

      Additional Details

      Usability

      IBM

      No answers on this topic

      Open Source

      Support of multiple components and ease of development.
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      Support Rating

      IBM

      IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.
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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

      IBM

      No answers on this topic

      Open Source

      Use of cloud for better execution power is recommended.
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      Alternatives Considered

      IBM

      We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.
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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

      IBM

      • Create secure business environment.
      • Save upto 90% of manual labor.
      • Improve my sales and marketing ROI.
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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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