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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data drift?
💡 Hint: Think about how the model's input data can change.
Question 2
Easy
Name a tool used for monitoring machine learning models.
💡 Hint: Consider the tools we discussed for tracking performance.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the main reason for monitoring machine learning models?
- To increase compute resources
- To maintain accuracy
- To enhance training speed
💡 Hint: Think about why we need to ensure consistent performance.
Question 2
True or False: Latency is the number of predictions a model can handle per second.
- True
- False
💡 Hint: Recall the definitions of latency and throughput.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a deployed a credit scoring model with noticeable accuracy drops, design a monitoring plan using tools to track and investigate issues.
💡 Hint: Focus on how to integrate these tools systematically in your monitoring strategy.
Question 2
Explain how to determine when retraining of a machine learning model is necessary and the strategies involved in setting up a feedback loop.
💡 Hint: Consider both automatic and manual aspects of monitoring and model updating.
Challenge and get performance evaluation