Question: Based on the text, explain why building a machine learning system is fundamentally an iterative loop rather than a straightforward linear process. What typical steps are involved in this loop?

Sample answer: Building a machine learning system is highly iterative because initial ideas rarely work perfectly on the first try. The development process requires a constant feedback loop. First, a developer starts with an idea on how to build or improve the system. Next, they implement that idea in code. After that, they carry out an experiment to measure how well the idea worked. Since the first few ideas usually fail, the developer must use the learnings from that experiment to generate new ideas. This cycle repeats continuously, often requiring dozens of attempts before a satisfactory solution is found.

Key points:

• The core steps are Idea, Code, and Experiment.
• The first few ideas typically do not work.
• Learnings from experiments inform the generation of new ideas.
• The cycle repeats dozens of times until a satisfactory outcome is achieved.

Rubric: A full-credit response must identify the Idea -> Code -> Experiment cycle, explicitly state that initial ideas usually do not work, and explain that learnings from experiments are used to generate new ideas in a continuous loop.