Jupyter Notebooks are best known as tools for Data Scientists to display Python, Spark or R scripts.
A Jupyter Notebook enables you to share words, images, code AND code results. .NET interactive Jupyter notebooks add c sharp, F# and PowerShell core to the mix.
In this episode, Rob Sewell will introduce Jupyter Notebooks and show you how useful they could be for you in your daily work-life for Incident Resolution, Repeatable Tasks, and Demoing New Features.
Video index:
[01:04] Why use notebooks
[02:01] Demo
[04:15] Display notebooks in GitHub
[04:52] Get your own .NET Interactive notebook
[05:26] Edit .NET Interactive notebooks in Azure Data Studio
Vicky Harp joins Scott Hanselman to show how Azure Data Studio combined the simple and robust SQL query editing experience of tools like SSMS with the flexibility and collaboration of Jupyter Notebooks. The November 2019 release of Azure Data Studio included SQL Server 2019 Guide as a Jupyter Book, which provides a richer troubleshooting experience.
Jeffrey Mew shows you how you can can natively edit Jupyter notebooks in Visual Studio Code.
Jupyter (formerly IPython) is an open-source project that enables you to easily combine Markdown text and executable Python source code on one canvas called a notebook.
These notebooks contain live code, equations, visualizations and narrative text. Jeffrey shows how easy it is to work with Jupyter notebooks in Visual Studio Code.
Here are some of the ones that caught my attention.
SQL Server 2019 Querying 1 TRILLION rows
OneTrillionRowsWarm.ipynb – This notebook shows how SQL Server 2019 reads 9 BILLION rows/second using a 1 trillion row table using a warm cache,
OneTrillionRowsCold.ipynb – This notebook shows how SQL Server 2019 performs IO at ~24GB/s using a 1 trillion row table with a cold cache.
Big Data, Machine Learning & Data Virtualization
SQL Server Big Data Clusters – Part of our Ground to Cloud workshop. In this lab, you will use notebooks to experiment with SQL Server Big Data Clusters (BDC), and learn how you can use it to implement large-scale data processing and machine learning.
Data Transfer – Spark to SQL using Spark JDBC connector
Data Transfer – Spark to SQL using MSSQL Spark connector
Configure – Configure a spark session using a notebook
Install – Install 3rd party packages
Restful-Access – Access Spark in BDC via restful Livy APIs
Machine Learning
Powerplant Output Prediction – This sample uses the automated machine learning capabilities of the third party H2O package running in Spark in a SQL Server 2019 Big Data Cluster to build a machine learning model that predicts powerplant output.
Ever wondered what breed that dog or cat is? In this show, you’ll learn how to train, optimize and deploy a deep learning model using Azure Notebooks, Azure Machine Learning Service, and Visual Studio Code using Python. Using transfer learning to retrain a mobilenet model via Tensorflow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset.
Next, watch how to optimize that model using the Azure Machine Learning Service HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.
Here’s an interesting talk from PyCon Germany by Joshua Görner, a Data Scientist at BMW.
From the video description:
Interactive notebooks like Jupyter have become more and more popular in the recent past and build the core of many data scientist’s workplace. Being accessed via web browser they allow scientists to easily structure their work by combining code and documentation. Yet notebooks often lead to isolated and disposable analysis artifacts. Keeping the computation inside those notebooks does not allow for convenient concurrent model training, model exposure or scheduled model retraining. Those issues can be addressed by taking advantage of recent developments in the discipline of software engineering. Over the past years containerization became the technology of choice for crafting and deploying applications. Building a data science platform that allows for easy access (via notebooks), flexibility and reproducibility (via containerization) combines the best of both worlds and addresses Data Scientist’s hidden needs.
In this tutorial, recent guest on Data Driven, Micheleen Harris demonstrates common Python libraries for image manipulation are introduced and used in a Jupyter notebook for manipulating a sample image.
Jupyter notebooks are great. They are interactive, customizable and can be made to beautifully illustrate data.
Unfortunately only a small fraction of data scientists takes the full advantage of the possibilities that they bring. In this talk, Jakub Czakon shows you some of the coolest notebook features that will impress your peers, dazzle your clients and make your work a lot more enjoyable.
