Drools vs. InRule vs. Pytorch

As an open source rule engine and product suite, Drools is well suited for the small and middle scale business to manage and integrate the rules to build the rule-driven system which can process the business-critical data and events to produce the automated decision. It is better to use Drools in the well-secured environment (back-end behind the DMZ), not putting it on the customer-facing front or exposing it directly the to public where may bring direct security risk in the enterprise environment. Drools still needs a lot hardening on the security side.

Incentivized

With InRule, we are expecting to be able to move business logic out of the developer domain and back into the business domain. Business logic is currently captured in UI (data validation) and middleware layers. These are areas in any application where leveraging InRule's capabilities allow for changes in business logic to be made with little or no IT involvement.

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

• Writing rules with business focus
• Rules evolution and maintenance
• separate business logic from program code

Incentivized

• It is easy to tweak business rule parameters.
• Create new logic with low barriers.

Incentivized

• 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

• Fusion doesn't support persistence of working memory, which brings some extra high availability risk to our business.
• Guvnor still has a lot room to be implemented, it is not so user-friendly for non-technical people, so a lot of business users complain it is hard to master.
• Rule execution server doesn't even have JMX implemented, hard to be monitored.
• Drools is still lacking support for key Web services standards.

Incentivized

• Needs more debugging capabilities.

• 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 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

InRule's Support Portal provides a "one stop shop" for submitting support questions, accessing training information, managing licenses, and getting updates on InRule's roadmap.

I did not participate in drools choice. I can only compare drools with the previous situation which was using nothing.

Incentivized

InRule offers a more organized software design, a well-structured framework in design, and is easier for new users to start contributing given documentation. Drools is spreadsheet-based and lacking the capability to do really advanced pseudo-programming.

Incentivized

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

• The IT department quickly adopted Drools as it is a very good java-based rule engine, which saves a lot of time to meet the project timeline and balanced our business requirements.
• Recently we start considering the OpenRules, which may be more business user-friendly.

Incentivized

• It is always reliable and efficient.
• Has won customers over.

• 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