OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.
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TensorFlow
Score 8.9 out of 10
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TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
If you do not have a large budget and are a large organization, I would steer clear of Actuate. If you are looking to do very complex washboarding, I would not use them. Your developers have to be very skilled to work with this. Plan to bring in consultants if necessary to help your process. Adhoc reporting is weak. If your pricing is user based and you expand, this could be very expensive.
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).
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.
I am no longer working for the company that was using Actuate but I believe they would continue to use it because the stitching costs would be to high. It would require a complete rewrite of the reports and the never version of Actuate (BIRT) even required an almost complete report rewrite
It is quite intuitive to use. It is fit specifically for doing sentiment, emotion, and intention analysis as well as text classification and text summarization. I would have given 10 if it is fit for the purpose of doing image processing and analysis as well. There is a huge market to analyze video and image data.
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.
It is vastly superior to these in many ways, for complex reporting it is a much more sophisticated solution. Visualizations are very good. Javascript extensibility is very powerful, others don't support this or as well. Pentaho and MS are both OLAP oriented. Pentaho is moving more toward big data, which was not our primary focus. Others are stuck in the Crystal Reports Band metaphor.
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
Actuate can handle 50 to 60 sub reports inside a report very well.
Dynamically creating the datasource, chart, graph, reports are the main advantages. We can do any level of drilling, and can create a performance matrix dashboard efficiently.
