Likelihood to Recommend It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
Read full review 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).
Read full review Pros Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc. SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly. Enforced best-practices set up POCs for deployment in production with a minimum of re-work. Estimator validation lets data scientists test and prove different models. Read full review 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. Read full review Cons The cost is steep and so only companies with resources can afford it It will be nice to have Chinese versions so that Chinese engineers can also use it easily It takes a while to learn how to input different kinds of skin defects for detection Read full review 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. Read full review Likelihood to Renew because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
Read full review Usability The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
Read full review Support of multiple components and ease of development.
Read full review Reliability and Availability From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
Read full review Performance Never had slow response even on our very busy network
Read full review Support Rating I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
Read full review 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.
Read full review In-Person Training The trainers on the job are very smart with solutions and very able in teaching
Read full review Online Training The Platform is very handy and suggests further steps according my previous interests
Read full review Implementation Rating It surprised us with unpredictable case of use and brand new points of view
Read full review Use of cloud for better execution power is recommended.
Read full review Alternatives Considered The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
Read full review 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
Read full review Scalability It helped us in getting from 0 to DSX without getting lost
Read full review Return on Investment Could instantly show data driven insights to drive 20% incremental revenue over existing results Still don't have a real use case for unstructured data like twitter feed Some of the insights around user actions have driven new projects to automate mundane tasks Read full review 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. Read full review ScreenShots