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Score 8.1 out of 100
36 Ratings
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Score 7.5 out of 100

Likelihood to Recommend

IBM Watson Machine Learning

Wherever you have a well qualified, segregated, data set with a clear problem definition any machine learning tool can be deployed. However, the key is to help the client define the problem to create the baseline on the performance and show improvements. It may so happen that we may not need a machine learning tool. This is where the execution of IBM Watson Machine Learning is lacking.
Vinay Pushkarna, B.Eng, MBA | TrustRadius Reviewer

TensorFlow

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 networks2. 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).
Anonymous | TrustRadius Reviewer

Pros

IBM Watson Machine Learning

  • Good machine learning tool
  • Easy integration
Vinay Pushkarna, B.Eng, MBA | TrustRadius Reviewer

TensorFlow

  • 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.
Nitin Pasumarthy | TrustRadius Reviewer

Cons

IBM Watson Machine Learning

  • IBM Watson Machine Learning delivery is challenging
  • IBM Watson's deployment business skill gap
Vinay Pushkarna, B.Eng, MBA | TrustRadius Reviewer

TensorFlow

  • 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.
Nisha murthy | TrustRadius Reviewer

Usability

IBM Watson Machine Learning

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 9.0
Based on 1 answer
Support of multiple components and ease of development.
Anupam Mittal | TrustRadius Reviewer

Support Rating

IBM Watson Machine Learning

IBM Watson Machine Learning 4.0
Based on 2 answers
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.
Vinay Pushkarna, B.Eng, MBA | TrustRadius Reviewer

TensorFlow

TensorFlow 9.1
Based on 3 answers
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.
Anonymous | TrustRadius Reviewer

Implementation Rating

IBM Watson Machine Learning

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 8.0
Based on 1 answer
Use of cloud for better execution power is recommended.
Anupam Mittal | TrustRadius Reviewer

Alternatives Considered

IBM Watson Machine Learning

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.
Vinay Pushkarna, B.Eng, MBA | TrustRadius Reviewer

TensorFlow

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
Anonymous | TrustRadius Reviewer

Return on Investment

IBM Watson Machine Learning

No answers on this topic

TensorFlow

  • 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.
Shambhavi Jha | TrustRadius Reviewer

Pricing Details

IBM Watson Machine Learning

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

TensorFlow

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

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