Practice Distributed Machine Learning (12.3) - Scalability & Systems - Advance Machine Learning
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Distributed Machine Learning

Practice - Distributed Machine Learning

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define data parallelism in your own words.

💡 Hint: Think about how teams might divide a project.

Question 2 Easy

What is the main advantage of model parallelism?

💡 Hint: Consider the resources available to each machine.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is data parallelism?

A method of splitting a model across nodes
Processing data across multiple nodes simultaneously
A method of distributing tasks among users

💡 Hint: Focus on how the data is divided.

Question 2

True or False: Model parallelism is used when a model can fit into a single machine's memory.

True
False

💡 Hint: Think of the model's size in relation to memory.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a distributed machine learning system for training a large image classification model. Specify how you would implement both data and model parallelism.

💡 Hint: Consider how large datasets and models will be divided to maintain efficiency.

Challenge 2 Hard

Evaluate a distributed machine learning framework and discuss the strengths and weaknesses of its data and model parallelism strategies.

💡 Hint: Look at how the framework uses both types of parallelism and theorize on their implications.

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