Azure ML workspaces
An Azure ML workspace allows you to build, deploy, and manage ML models at scale. It provides a centralized workspace for data scientists, machine learning engineers, and developers to collaborate on machine learning projects, with the following features:
- An Azure ML workspace is an end-to-end suite for organizing and managing ML assets such as datasets, models, notebooks, experiments, and pipelines/resources. It provides a centralized location for team collaboration, version control, and resource management.
- It integrates with Jupyter notebooks and provides an interactive environment for developing and running code, visualizing data, and documenting the ML process.
- It supports dataset versioning and management so you can register and track different versions of datasets for ML model training and evaluation. Datasets can be stored within the workspace or referenced from external data sources.
- It allows you to organize and track different iterations...
