The Azure Machine Learning command-line interface (CLI) facilitates the retrieval of trained machine learning models from an Azure Machine Learning workspace to a local machine or compute environment. This process involves specifying the model’s name and, optionally, the version, enabling users to access and utilize the model’s trained parameters for tasks such as inference or further analysis outside of the Azure Machine Learning environment. For instance, a user might employ this functionality to obtain a model trained for image classification, making it available for deployment within an edge device application.
The ability to acquire models programmatically offers significant advantages in automation and deployment pipelines. It enables seamless integration with continuous integration and continuous delivery (CI/CD) systems, allowing for automated testing, versioning, and deployment of machine learning models. Furthermore, this capability fosters collaboration by allowing data scientists and engineers to share and reuse trained models effectively. Historically, manually transferring model files introduced risks of corruption or version mismatch; this streamlined method mitigates those risks, improving efficiency and reliability.
