What are the three high-level phases of the AI model lifecycle emphasized in CPMAI?

What are the three high-level phases of the AI model lifecycle emphasized in CPMAI?

• A. Data preparation, model development and evaluation, and deployment and monitoring
• B. Data collection, feature engineering, and deployment
• C. Data labeling, model arch, and reporting
• D. Requirements, design, and testing

Answer: (B)

The main idea here is the broad sequence that drives how an AI model comes to life in CPMAI: data collection, feature engineering, and deployment. Start by gathering and curating the data you’ll use, focusing on quality, representativeness, and any labeling needs. Next, transform that data into meaningful inputs for the model through feature engineering—creating, selecting, and preparing features that let the model learn effectively. Finally, deploy the model into production and establish ongoing monitoring and governance to maintain performance and safety over time. Other options mix in activities such as model evaluation, architecture, or software design, which aren’t the three high-level phases CPMAI emphasizes.

The main idea here is the broad sequence that drives how an AI model comes to life in CPMAI: data collection, feature engineering, and deployment. Start by gathering and curating the data you’ll use, focusing on quality, representativeness, and any labeling needs. Next, transform that data into meaningful inputs for the model through feature engineering—creating, selecting, and preparing features that let the model learn effectively. Finally, deploy the model into production and establish ongoing monitoring and governance to maintain performance and safety over time.

Other options mix in activities such as model evaluation, architecture, or software design, which aren’t the three high-level phases CPMAI emphasizes.