20.4.1 - Why Monitoring is Crucial
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is data drift?
💡 Hint: Think about changes in the inputs over time.
What do we monitor to keep our machine learning models effective?
💡 Hint: Consider the important factors that reflect model performance.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary reason for monitoring machine learning models?
💡 Hint: Consider what monitoring keeps intact over time.
True or False: Concept drift refers to changes in the input data distribution.
💡 Hint: Differentiate between input data changes and relationship changes.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Develop a plan for implementing an automated monitoring system for a deployed ML model. What metrics would you include, and how would you respond to alerts?
💡 Hint: Think about what critical indicators could indicate a need for action.
Design a study that tests the impact of data drift on model accuracy using historical data. Outline your methodology.
💡 Hint: Determine how to quantify changes in prediction accuracy against shifting data.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.