IFS Applications vs. TensorFlow

Order to cash processes and scenario are implemented natively in IFS. An HR module exists for career management also (objectives, comportments, training, mobility) but may be improved in terms of workflow validation (e.g. training to validate by a manager) or reporting.

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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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• It supports the web version in most later versions.
• It provides various mobile versions of the applications as mobile apps to cater various areas.
• IFS Applications allows easy configurations of the application without going for costly customization.
• It covers almost every areas of an industrial company so any other third party applications are minimum.
• It allows safer and easier built-in options for integrations capabilities.

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• 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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• Handling massive volumes of data can be improved.

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• 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.

The system is very difficult to use, overly complex, and difficult to learn. It also has limitations that are hard to overcome.

Support of multiple components and ease of development.

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We have been unable to get answers to our questions, solutions to our problems, and they don't seem interested in working in the construction industry.

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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Use of cloud for better execution power is recommended.

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IFS Applications is based on Agile Technology which allows organizations to reconfigure user interface as per user requirements and make it user-friendly. Other applications are lagging on many fronts like User Interface, Online help document availability, Implementation methodology, and post-implementation expenses.

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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• Reporting is now centralized and managed. Previously, reports were outside the information systems and there was a risk of incoherence.
• Accounting controls are now in place on the overall processes, including production, which helped the company to reduce closing periods or to produce more easily official mandatory accounting files yearly.
• Interfaces between the CRM forecast tool and IFS helped to keep the tools in sync, and to decrease the processing times prior to production launch.

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• 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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