This post is co-authored by Sharon Xu Program Manager, Azure Notebooks.
Today we are very proud to announce the next set of productivity features and improvements for the notebook experience. Since we announced the GA release of Notebooks in Azure Machine Learning (Azure ML), we have learned a lot from our customers. Over the past few months, we have incrementally improved the notebook experience while simultaneously contributing back to the open source nteract project. The Azure ML team recently released a robust set of new functionalities designed to improve data scientist productivity and collaboration in Azure ML Notebooks.
Data scientist & Developer Productivity
We have spoken to several data scientists and developers to fully understand the additional features needed to improve productivity while developing machine learning projects. From feedback, we have found that users constantly needed the following enhancements to speed up their workflow: a clear indication that a cell has finished running, a way to templatize common code excerpts, a way to check variable contents, and more. The following list is a culmination of the most highly requested productivity features:
Cell Status Bar. The status bar located in each cell indicates the cell state: whether a cell has been queued, successfully executed, or run into an error. The status bar also displays the execution time of the last run.
Variable Explorer. provides a quick glance into the data type, size, and contents of your variables and dataframes, allowing for quicker and simpler debugging.
Figure 1: (1) Cell status bar (2) Variable explorer
Notebook snippets (preview). Common Azure ML code excerpts are now available at your fingertips. Navigate to the code snippets panel, accessible via the toolbar, or activate the in-code snippets menu using Ctrl + Space.
Figure 2 (1) Notebook snippets panel, showing all useful snippets
IntelliCode. IntelliCode provides intelligent auto-completion suggestions using an ML algorithm that analyzes the context of your notebook code. IntelliCode suggestions are designated with a star.
Table of Contents. For large notebooks, the Table of Contents panel then allows you to navigate to the desired section. The sections of the notebook are designated by the Markdown headers.
Markdown Side-by-side Editor in Notebooks. Within each notebook, the new side-by-side editor allows you to view the rendered results of your Markdown cells directly in your notebook editing.
Figure 4: (1) Table of content pane (2) Markdown side by side
Collaboration and Sharing
An increasing number of data scientists and developers are creating notebooks collaboratively and sharing these notebooks across their team We heard feedback that most users feel like they are missing adequate tools to edit notebooks simultaneously or share their notebooks with a broader audience. Users often resort to screen shares and calls to complete or present work within a notebook. We recently just release a few new features to help combat some of these issues:
Co-editing (preview). Co-editing makes collaboration easier than ever. The notebook can now be shared by sending the notebook URL, allowing multiple users to edit the notebook in real-time.
Figure 5: Live Co-editing in Azure ML
Export Notebook as Python, LaTeX or HTML. When you feel satisfied with the results from your notebook and ready to present to your colleagues, you can export the notebook to various formats for easy sharing. LaTeX, HTML, and .py are currently supported.
Figure 6: Export Notebooks as Python and more in Azure ML
Get Started Today
To begin using these features in Azure ML Notebooks, you will first need to create an Azure Machine Learning. Your Azure ML workspace serves as your one-stop-shop for all your machine learning needs, where you can create and share all your machine learning assets.
Once you have your workspace set up, you can get started using all the features in the Azure ML Notebooks experience. The notebooks experience aims to provide you with an integrated suite of data science tools. Users can start working with a highly productive and collaborative Jupyter notebook editor directly in their workspace as well as quickly access other ML assets such as experiment details, datasets, models, and more.
With the addition of this host of features, notebooks in Azure ML aims to improve every aspect of your development needs – collaboration, code editing, debugging. Give these features a try and leave your feedback. The feedback provided by our community is what drives us to improve and build new features. As we continue to push out new releases, keep an eye out, because the team has a few more exciting features coming out soon.