You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?
A.
Use your model to run predictions on fresh customer input data.
B.
Test and evaluate your model on your curated data to determine how well the model performs.
C.
Monitor your model performance, and make any adjustments needed.
D.
Delineate what data will be used for testing and what will be used for training the model.
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a productionenvironment. References: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle
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