InterSystems IRIS vs. TensorFlow

Intersystems IRIS is a really great tool for Interoperability. It has so many capabilities out of the box and then such a great developer community on top of that, that there are really no limits to what you can do in terms of data manipulation and translation. Personally I find it to be a great tool if you are looking for Interoperability software.

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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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• Connecting with mutliple vendor platforms and systems allowing us to consume and provide data as needed.
• Allowed us to implement our own RESTful api engine to server data to our internal applications.
• Setting up their recommendations for high availability allow us to perform server maintenance with minimal down time to our users.

• 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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• Enhanced documentation, more comprehensive and user-friendly documentation, including detailed tutorials and examples
• Improving compatibility and integrations with others programming languages
• Introducing tools and techniques to optimize the performance of ObjectScript applications, such as profiling tools, performance monitoring utilities, and code optimization guidelines
• A better compatibility with Python libraries

• 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 interface is very intuitive, the documentation is very good so it is not complicated to operate.

The security is complex, but you can create a special role to access and the user ONLY can operate with the part that it allows.

Also, you can examine the data very quick with the SQL Browser integrated.

Support of multiple components and ease of development.

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The InterSystems WRC has always been helpful and responsive. The folks I have spoken with are always understanding of our needs and questions and regardless of if the question is simple or complex we are always met with the same professionalism and helpfulness every time. I have no hesitations contacting InterSystems for help!

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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Will done based on a proper planning, so this will makes execution more easier and better.

Use of cloud for better execution power is recommended.

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We are using InterSystems IRIS [especially] for database operations as the query performance is really good for [a large] amount of customer data. You can easily integrate for any application like web, desktop, and many more. It also provides BI functionality which is also very easy to implement using InterSystems IRIS[.]

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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• Very Fast and scalable software to deal with big data flow, safe and robust.
• The studio IDE is quite old fashion and lacks a few facilities
• amazing for complex data consumption, its milyi-model capability wich allows multiple different models for a single set of codes.

• 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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