Practice Case Studies In Scalable Ml Systems (12.9) - Scalability & Systems
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Case Studies in Scalable ML Systems

Practice - Case Studies in Scalable ML Systems

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does TFX stand for?

💡 Hint: Think about the full name of the framework.

Question 2 Easy

What is one feature of Michelangelo?

💡 Hint: Consider what aspects of ML can be automated.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does TFX stand for?

TensorFlow Execution
TensorFlow Extended
TensorFlow Experiment

💡 Hint: Think about the purpose of the framework.

Question 2

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

True
False

💡 Hint: Review what aspects of ML deployment are considered in Michelangelo.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Think about the specific stages of ML from data collection to model deployment.

Challenge 2 Hard

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

💡 Hint: Consider how you would gather and analyze user interactions over time while deploying both models.

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Reference links

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