A machine learning (ML) model is not "build once and done." A good model, like a living product, is born, learns, is evaluated, goes into the world, and continues to be monitored. This entire flow is called the ML Lifecycle. The AIF-C01 exam does not expect you to code models directly. Instead, it requires you to clearly understand "what stages does the journey from data to operations involve, and what is each stage responsible for?"
Today we organize the six stages of the ML lifecycle—Data Collection → Data Preparation → Model Training → Model Evaluation → Deployment → Monitoring—into one circular diagram.