Designing and Implementing a Data Science Solution on Azure — Question 2

You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
✑ Models must be built using Caffe2 or Chainer frameworks.
✑ Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network.
You need to select a data science environment.
Which environment should you use?

Answer options

Correct answer: A

Explanation

The correct choice is Azure Machine Learning Service because it provides robust support for building and managing machine learning models using frameworks like Caffe2 and Chainer. It also ensures that data scientists can work effectively both online and offline, allowing for seamless pipeline updates. The other options, while useful, do not fully meet the specified requirements for personal device support and model framework compatibility.