Machine Learning and Data Science are the hot technologies everyone is chasing this year, but sharing Machine Learning solutions is more complicated than simple source control. How do you share the process that allowed you to arrive at your solution? How do you effectively communicate between Data Scientists and Developers? How do I make it pretty so that I can present the work to non-technical stakeholders?

This talk answers these questions using Azure Notebooks. We will walk through a real example of a Jupyter Notebook, its features and I how I created it. The topics covered include:

• What are Azure Notebooks? How do they fit into the Azure Ecosystem?
• What is Jupyter? What are it’s strengths and weaknesses?
• Mixing code snippets and execution results
• Data Visualization for presentation and analysis
• Markdown for exposition and formatting
• Sharing and Source Control

You’ll leave with an understanding of Jupyter and Azure Notebooks and understand how to apply Azure Notebooks to real-world problems.

TARGET AUDIENCE: Developers, Architects, Business Analysts, Data Scientists, Data Developers