Impacting businesses through predictive models

Nearly ~90% of models never make to production leading to business impact and further impacting the top line and bottom line of the businesses.

Below are few reasons:-

• Model Development:- We might take lot of time to build and then during the process business requirements change therefore, the need of the model is no more.
• Model Deployment:- Once we development, then comes the turn to deploy the model in the business processes. This requires special skills such building data pipelines, model hosting and scaling the platforms. It is possible that we don’t have right people or skills available in the organisation.
• Model Approval:- Once, it is deployed, then we need to check the performance of the models to meet the required business expecactions. If it doesnt meet the required business expectation then again it is possible that it wont be approved by Stakeholders.

We can navigate the challenges to create the business impact through predictive models. One of the challenging problem in creating the impact is to build the robust machine learning models which translates to required business expectation. This can be addressed from more theoretical approach as well. This paper talks about the Robust Machine Learning.