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4 cards from this section. Quick, good the night before a test.
What is a container in the context of machine learning?
Think about what is required to execute a machine learning model.
Name a popular tool used for containerization.
This tool is well-known for its ability to package applications.
What is the primary purpose of Docker?
Think about what Docker mainly focuses on.
True or False: Kubernetes can automatically scale containerized applications based on user demand.
Consider automation features of orchestration tools.
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.
Consider every step from building to monitoring the deployment.
You notice that the performance of a deployed ML model is degrading. How would you leverage Docker and Kubernetes to maintain its reliability?
Think about how version control and rollback strategies might work in this context.
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