Question

Your data science team needs a reproducible training environment in Azure Machine Learning that includes scikit-learn plus two internal Python packages hosted on a private Azure Artifacts feed. The environment must be versioned as a reusable asset so that all training jobs reference the exact same dependency set. Which approach should you use to create and register this environment?

Accepted Answer

Define a custom environment from a base Docker image plus a conda specification that installs the internal packages from the private feed, then register it as a versioned environment asset in the workspace.

Answer

Select an Azure ML curated environment for scikit-learn and reference it directly in each job, since curated environments are automatically versioned.

Answer

Install the internal packages manually on the compute cluster nodes via a startup script so every job inherits them without defining an environment.

Answer

Create a data asset that packages the wheel files for the internal packages and mount it into each job at runtime.