Amazon SageMaker vs. InRule

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

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.

• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

Incentivized

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

Incentivized

• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Incentivized

• Needs more debugging capabilities.

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.

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

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

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Incentivized

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