You are leading a project to develop a machine learning (ML) solution for a financial services company that aims to automate loan approval processes. The project has reached a stage where the trained ML model needs to be made operational within the company's existing loan processing system. This system is critical for the company's operations and requires high availability, scalability, and compliance with financial regulations. Considering these constraints, which phase of the ML solution architecture is primarily focused on integrating the ML model into the existing loan processing system to ensure seamless operation, compliance, and scalability? Choose the best option.

Real Exam

Data pre-processing, as it involves preparing the loan application data for model training.

22.9%

Problem formulation, where the business problem of automating loan approvals is defined and scoped.

8.3%

Model deployment, which involves integrating the trained model into the loan processing system and ensuring it meets operational requirements.

43.8%

Model evaluation, where the model's performance is assessed against a test dataset of loan applications.

14.6%

Both A and D are correct, as data pre-processing and model evaluation are critical before deployment.

10.4%

Google Professional Machine Learning Engineer