AIF-C01 Question 5
Single answerA data scientist is building a recommendation system for an e-commerce platform. They decide to use machine learning for this task. During a team meeting, the scientist explains that the model will require training to learn patterns in the data and will later be used for inferencing to make predictions for new users. Which of the following statements best describes the terms 'training' and 'inferencing' in this context?
- A
Training involves using labeled data to teach the model, while inferencing is the process of using the trained model to make predictions on unseen data.
- B
Training is the process of feeding the model with raw data so it can automatically identify patterns, while inferencing involves manually evaluating the model on test data.
- C
Training is the process of manually defining rules for the model to follow, while inferencing is when the model applies these rules to new data.
- D
Training refers to testing the model on unseen data, and inferencing refers to adjusting the model's parameters based on errors.
Show answer and explanation
Correct answer: A
Explanation
Understanding the difference between training and inferencing is key to working with machine learning models. Training is the process where the model learns patterns from labeled data by adjusting its internal parameters. Inferencing refers to the model's application phase, where it uses the learned patterns to make predictions on new, unseen data. This distinction is essential for implementing and explaining machine learning workflows effectively.
- A. Correct.
Correct. Training is the process of using labeled data to teach the model to learn patterns, while inferencing is when the trained model is used to make predictions on new, unseen data.
- B. Incorrect.
Incorrect. While training involves labeled data, inferencing is not about manually evaluating the model but rather about using it to make predictions.
- C. Incorrect.
Incorrect. Training does not involve manually defining rules; it involves learning patterns from data automatically. Inferencing also does not involve applying manually defined rules.
- D. Incorrect.
Incorrect. Training is not about testing the model on unseen data, and inferencing does not involve adjusting model parameters.