If you run your own infrastructure in a private cloud with one of the larger operators such as AWS or Azure, you may have noticed some time ago came support for ARM64 processors.
The DecisionRules cloud runs partially in some locations on AWS T4g instances, which are available in many locations. T4g instance types use AWS Graviton 2 processors. We try to combine different types of AWS instances that suit a given site.
Why do we use ARM64? - Cheaper operation of infrastructure while maintaining comparable performance. That's why we decided to publish ARM compatible containers for our On-Premise customers as well.
AWS promises 40% savings over other Intel-based instances. In our case, the savings are lower, but still noticeable on the larger number of instances that we run or need to create on-demand with increased traffic to our APIs.
DecisionRules docker containers are published on DockerHub. Here, as with other containers, you see the supported architecture.
Our customers and their technology scouts often use Apple M1 processors, and without ARM compliant containers, DecisionRules was slower to run as instructed because emulation via Rosetta 2 was required. This issue is now resolved and you can easily run DecisionRules showcase on your Apple M1 chip.
Uncover the impact of DecisionRules in enhancing the operations of The Mix, a UK-based e-commerce enterprise specializing in Garden and Home Furniture. By overcoming challenges in customer prioritization, product information validation, and text precision, DecisionRules introduced scalability, flexibility, and functionality. Through automated repricing, text accuracy checks, client scoring, barcode validation, and product information verification.
Solving rules is one thing. But imagine that you could access the history of the rule solver and monitor its activity with BI tools. Well, as of now, you can.
In this article, we will showcase the use of DecisionRules in a financial services environment, more specifically in the approval process for applications for lending products. Unlike in the sale of consumer goods, when customers want to “buy” a credit product they need to be accepted by the institution providing the funds – the approval process is made up of a series of go/no-go decisions based on the perceived level of risk of the customer, suitability of product, and pricing (commensurate with level of risk).