Apply the previous explanations in a guided problem.

Follow how examples, labels, goals, loss, and learned parameters turn past data into a model that can act on new inputs. You will separate training, where behavior is created, from inference, where behavior is used.

Inspect how missing cases, noisy labels, biased samples, and unclear targets change what a model learns to do. You will connect data quality directly to system behavior and user-visible mistakes.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Turn model outputs like scores, probabilities, rankings, or generated text into real product decisions. You will reason through thresholds, confidence, abstaining, human review, and safe fallback behavior.

Trace how a large language model uses tokens, prompts, context, and next-token prediction to produce responses. You will recognize why prompt wording, retrieved context, and randomness can change the same model’s behavior.

Apply the previous explanations in a guided problem.

Use test sets, metrics, edge cases, and qualitative review to judge whether an AI system is reliable enough for its purpose. You will distinguish an impressive demo from evidence that the system behaves well across realistic cases.

Follow how logs, user feedback, monitoring, drift detection, and retraining keep an AI system useful after launch. You will see feedback as part of the system design, not just a future improvement task.

Apply the previous explanations in a guided problem.

Check your understanding with a short quiz.

Review this chapter with practice based on your mistakes.