What you need to know about Azure Notebooks

Modern business applications bring together many strands of development. You’re no doubt most familiar with n-tier applications, building on decades of programming skills and techniques, linking UI to code and to data. They’re familiar and easy to understand. But that all changes when you start to add new technologies and approaches, constructing massively scalable distributed computing platforms that take advantage of large amounts of data and machine learning.

Much of modern machine learning builds on using analytical tools to explore data and develop rules for showing statistically significant outliers. Although specialized neural networks handle complex speech and image recognition, most problems don’t require particularly complex models—especially if you’re using predictive algorithms on streams of data from sensors or other IoT hardware. Even so it’s important to try new algorithms out on realm data before you implement them.

Introducing Azure Notebooks

Getting to grips with machine learning can be tricky. It’s hard to visualize data at scale, and harder still to understand how analytics can drive machine learning. That’s where Azure Notebooks come in, giving you a place to explore analytics using familiar languages in a playground where you can try out code and visualizations, sharing results with colleagues, and adding descriptive text around your code and results for presentations to management and your team.

Azure Notebooks is an implementation of the widely used open-source Jupyter Notebook. Supporting more than 40 different languages, Jupyter Notebooks can run locally as well as on the cloud, and you can bring code that’s developed on Azure into a private Jupyter Notebook, ready for sharing on-premises—or if you need to work with cloud code on a plane.

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