A mid$-$sized e$-$commerce company relies heavily on machine learning $($ML$)$ models to drive its business forecasting, customer churn prediction, and product recommendation systems. All customer data stored in the e$-$commerce system are sensitive in nature. While the initial implementation of these systems demonstrated notable success, the company is now encountering several challenges, which have hindered its operational efficiency and the overall effectiveness of its ML infrastructure.

$[2+4+4+2=12$ Marks$]$

HS$4097458$

process. $$ Each update to the churn prediction model takes weeks to deploy due to a manual testing and integration

$$ Their recommendation system's accuracy has declined over time as customer preferences evolve.

$$ Redundant efforts and inefficiencies, no historical traces.

Questions

$5-140$a$)$ As

Poor communication causing friction among teams during the deployment phase.

an MLOps consultant, identify the root causes of these issues.

b$)$ Propose MLOps solution that addresses those problems.

c$)$ Explain how the proposed solution improves reliability and security.

d$)$ Describe the roles and responsibilities of peoples to ensure effective collaboration.