Amazon SageMaker is a fully managed machine learning platform that enables data scientists and developers to build, train, and deploy ML models. Users can share SageMaker Studio notebooks with peers for seamless collaboration.
Table of Contents
Question
Which statement about Amazon SageMaker is true?
A. Users can only train models, and not deploy them, on Amazon SageMaker.
B. Users can share SageMaker Studio notebooks with peers.
C. Users are responsible for managing the virtual machines in Amazon SageMaker.
D. Users cannot change the instance type on an existing SageMaker Studio notebook.
Answer
B. Users can share SageMaker Studio notebooks with peers.
Explanation
This statement about Amazon SageMaker is true. SageMaker Studio provides a collaborative environment where users can easily share Jupyter notebooks with peers and colleagues. This allows data scientists and ML engineers to work together seamlessly, share code and ideas, and collaborate on building and improving ML models.
A few key points about Amazon SageMaker:
- It is a fully managed platform that handles the infrastructure and DevOps required for the ML workflow, so users don’t have to manage servers, VMs, or clusters themselves.
- It supports the end-to-end ML process – users can build, train, tune, deploy, and monitor models all within SageMaker. Models can be deployed to production with a single click.
- Users can change instance types on existing notebooks to scale compute resources up or down as needed. Different instances can be used for notebook usage vs. model training and inference.
- It has built-in integration with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn, as well as other AWS services.
So in summary, the ability to easily share SageMaker Studio notebooks with peers for collaboration is a key feature of the platform. The other statements are incorrect – users can deploy models, do not manage VMs themselves, and can change instance types on notebooks.
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