Practice Containers and Orchestration - 20.2.3 | 20. Deployment and Monitoring of Machine Learning Models | Data Science Advance
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Containers and Orchestration

20.2.3 - Containers and Orchestration

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a container in the context of machine learning?

💡 Hint: Think about what is required to execute a machine learning model.

Question 2 Easy

Name a popular tool used for containerization.

💡 Hint: This tool is well-known for its ability to package applications.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary purpose of Docker?

To deploy models
To create containers
To orchestrate applications

💡 Hint: Think about what Docker mainly focuses on.

Question 2

True or False: Kubernetes can automatically scale containerized applications based on user demand.

True
False

💡 Hint: Consider automation features of orchestration tools.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Imagine you are tasked with deploying a new machine learning model using Docker and Kubernetes. Describe the steps you would follow to ensure a successful deployment.

💡 Hint: Consider every step from building to monitoring the deployment.

Challenge 2 Hard

You notice that the performance of a deployed ML model is degrading. How would you leverage Docker and Kubernetes to maintain its reliability?

💡 Hint: Think about how version control and rollback strategies might work in this context.

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