The enhanced notebook editor in the Azure Machine Learning Studio enables members of Azure ML workspace to edit, share and collaborate on the notebooks in the same environment that contains their ML experiments, metrics, models, datasets, and more. With the new Studio Notebooks, Data Scientists are one link away from collaboration. We believe this simplicity of sharing will be especially welcome in today’s world of mandatory remote work. The new notebook editor is based on an open-source interact project and provides full compatibility with standard Jupyter. The editor also brings some best-in-class code editor features from VS Code that our customers know and love. For the first time, Data Scientists can use advanced features like full IntelliSense and inline error highlighting directly in their Jupyter notebooks.
The Experiments UI in the Azure Machine Learning Studio enables users to inspect, analyze and troubleshoot their runs in the Azure ML workspace. With the new Cross-Experiment Run Comparison, Data Scientists can analyze their runs by graphically comparing and visualizing runs on the charts across some or all their experiments in a workspace. Users can also monitor their run progress, status and compare run performance which are essential tasks for both administrators and data scientists performing machine learning tasks. The new Custom Views allow users to customize their run analysis views and more effectively organize and keep track of their runs. The simplicity of sharing Custom Views allows for enhanced collaboration across colleagues and data scientist teams.
Abe Omorogbe, Program Manager at Microsoft
Abe Omorogbe - He is a Program Manager at Microsoft. He works within the AI Platform Group specifically on Azure Machine Learning - building exciting Machine Learning tools that make Data Scientist and ML Engineers more productive.
Shané Winner, Program Manager at Microsoft
Shané Winner – She is a Program Manager at Microsoft on the Azure Machine Learning platform team focused on improving the user experience and productivity for Data Scientists doing Machine Learning.
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