InterSystems IRIS vs. Pytorch

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.

Incentivized

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

Incentivized

• 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.

• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

Incentivized

• 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

• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

Incentivized

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.

The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

Incentivized

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!

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

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[.]

Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

Incentivized

• 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.

• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

Incentivized