AWS Certified AI Practitioner · Free Practice Question Medium
Question 31
A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.
Given this context, which statement best defines the use of MLflow with Amazon SageMaker?
-
A
Label data using human-in-the-loop
-
B
Leverage no-code ML
-
C
Manage machine learning experiments
-
D
Perform automatic model tuning
Reveal correct answer
Correct answer: C
Explanation
Correct option:
Manage machine learning experiments
Machine learning is an iterative process that requires experimenting with various combinations of data, algorithms, and parameters while observing their impact on model accuracy. The iterative nature of ML experimentation results in numerous model training runs and versions, making it challenging to track the best-performing models and their configurations.
Use MLflow with Amazon SageMaker to track, organize, view, analyze, and compare iterative ML experimentation to gain comparative insights and register and deploy your best-performing models.
Incorrect options:
Perform automatic model tuning - Automatic model tuning can be performed using SageMaker Automatic Model Tuning (AMT).
Label data using human-in-the-loop - Labeling data with a human-in-the-loop is performed using SageMaker Ground Truth.
Leverage no-code ML - SageMaker Canvas offers a no-code interface that can be used to create highly accurate machine learning models.
Reference:
Discussion
Think the marked answer is wrong, or have a better explanation? Share it below — comments appear after review.
