AI News, Caroline Hermon, head of adoption of artificial intelligence and ... artificial intelligence
- On 25. juni 2019
- By Read More
Should you be banking on open source analytics?
Essentially, if you build a credit risk model or a customer analytics application that depends on an open source package, your systems also depend on all the dependencies of that package.
If they make changes to their package, and those changes introduce a bug, or break compatibility with a package further up the dependency tree, or include malicious code, there could be an impact on the functionality or integrity of your model or application.
As a result, when a bank opts for an open source approach, it either needs to put trust in a lot of people or spend a lot of time reviewing, testing and auditing changes in each package before it puts any new code into production.
Each project is a powerful tool designed for a specific purpose: manipulating and refining large data sets, visualising data, designing machine learning models, running distributed calculations on a cluster of servers, and so on.
As a result, unless banks are prepared to invest in building a robust end-to-end data science platform from the ground up, they can easily end up with a tangled string of cobbled-together tools, with manual processes filling the gaps.
This quickly becomes a nightmare when banks try to move models into production because it is almost impossible to provide the levels of traceability and auditability that regulators expect.
The good news is that there’s a way for banks to benefit from the key advantages of open source analytics software – its flexibility and rapid innovation – without exposing themselves to unnecessary governance-related risks.
Solutions like SAS® Viya® allow data scientists to write code and build models in open source languages like R and Python, but run them in a controlled, enterprise-grade production environment, with full end-to-end traceability.
Crucially, the ability to innovate by moving from traditional regression models to a more accurate machine learning-based approach is estimated to deliver up to £16 million in financial benefits over the next three years.
- On 15. april 2021
One Hundred Years of Statistical Developments in Animal Breeding
Presented by Dr. Daniel Gianola - University of Wisconsin December 4, 2015 at Michigan State University.