Practice Case Studies in Scalable ML Systems - 12.9 | 12. Scalability & Systems | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does TFX stand for?

๐Ÿ’ก Hint: Think about the full name of the framework.

Question 2

Easy

What is one feature of Michelangelo?

๐Ÿ’ก Hint: Consider what aspects of ML can be automated.

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 does TFX stand for?

  • TensorFlow Execution
  • TensorFlow Extended
  • TensorFlow Experiment

๐Ÿ’ก Hint: Think about the purpose of the framework.

Question 2

True or False: Michelangelo includes features for A/B testing.

  • True
  • False

๐Ÿ’ก Hint: Review what aspects of ML deployment are considered in Michelangelo.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a scalable ML pipeline using TFX for a hypothetical e-commerce platform. Outline the key components and describe their functions.

๐Ÿ’ก Hint: Think about the specific stages of ML from data collection to model deployment.

Question 2

Evaluate a scenario where a model deployed via Michelangelo starts showing performance issues. How would you utilize A/B testing to address this?

๐Ÿ’ก Hint: Consider how you would gather and analyze user interactions over time while deploying both models.

Challenge and get performance evaluation