InterSystems IRIS vs. Pytorch

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
InterSystems IRIS
Score 7.7 out of 10
N/A
InterSystems IRIS is a complete cloud-first data platform that includes a multi-model transactional data management engine, an application development platform, and interoperability engine, and an open analytics platform. It is is the next generation of InterSystems' data management software. It includes…N/A
Pytorch
Score 9.3 out of 10
N/A
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A
Pricing
InterSystems IRISPytorch
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
InterSystems IRISPytorch
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeOptionalNo setup fee
Additional Details
More Pricing Information
Community Pulse
InterSystems IRISPytorch
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InterSystems IRISPytorch
Small Businesses
Google Cloud SQL
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InterSystems IRIS
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Score 7.7 out of 10
Medium-sized Companies
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Score 8.9 out of 10
Posit
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Score 10.0 out of 10
Enterprises
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Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
InterSystems IRISPytorch
Likelihood to Recommend
7.4
(0 ratings)
9.0
(0 ratings)
Likelihood to Renew
8.2
(0 ratings)
-
(0 ratings)
Usability
8.2
(0 ratings)
10.0
(0 ratings)
Support Rating
7.4
(0 ratings)
-
(0 ratings)
Implementation Rating
8.2
(0 ratings)
-
(0 ratings)
User Testimonials
InterSystems IRISPytorch
Likelihood to Recommend
It is best suited in the scenario where a single interface is required for providing [a complete end-to-end] solution to the customers. You don't need [a] separate platform to write code or [perform] database operations. All you need is InterSystems IRIS software and you are done. You can also use analytics functionality which is one of the greatest [features] which many customers need for their solution[.]
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Everything deep learning related if not on TPU (in such case, JAX would be better suited). For LLM deployment, libraries such as vLLM would be better suited, too; otherwise, wrapping the PyTorch model with Ray is a good option.
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Pros
  • 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.
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  • Provides Benchmark datasets to test your custom algorithm
  • Provides with a lot of pre-coded neural net components to use for your flow
  • Gives a framework to write really abstract code.
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Cons
  • 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
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  • It should have support for Java also as Java is one of the most popular language.
  • They should make things more easy if we want to use GPUs for computation.
  • They should keep adding the latest models so that we can easily load them for use for further fine-tuning.
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Usability
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.
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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.
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Support Rating
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!
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No answers on this topic
Implementation Rating
Will done based on a proper planning, so this will makes execution more easier and better.
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No answers on this topic
Alternatives Considered
Tibco was not originally planned to be used for HL7 Integrations and as such we had to create some very complicated processes in order for the messages to parse and validate appropriately. It was simply not built for this type of interoperability. Comparatively, InterSystems IRIS for Health (HealthConnect) has out of the box HL7 features that would parse messages, offer a variety of validation options, simplified data lookups and transformation and reduced the amount of time it took to develop connections with out vendor systems. InterSystems IRIS also allows one to push just single files into production at a time so there is less of a chance of us pushing something that should not be in production yet as our previous system was set up to with TIBCO deployments
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Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly less time to create valuable POCs as most of the things are inbuilt.
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Return on Investment
  • 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.
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  • Less time wasted on handling the library version issues
  • Small learning curve as very similar to Python
  • Compatibility with other popular Python libraries makes it easy to build a lot of things on it
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