Azure Notebooks - Jupyter for the Cloud
- 1. Azure Notebooks Jupyter For The Cloud
- 2. WELCOME
- 3. Cameron Vetter I have 20 years of experience using Microsoft tools and technologies to develop software. I have experience in many roles including Development, Architecture, Infrastructure, Management, and Leadership roles. I've worked for some of the largest companies in the world and for small local companies getting a breadth of experience in different Corporate Cultures. Currently, I work at SafeNet Consulting, where I get to do what I love... Architect, Design, and Develop great software! I currently focus on Microservices, SOA, Azure, Cognitive Toolkit, and Kubernetes. Principal Cloud Consultant
- 4. A Partner to Advise and Support About SafeNet Consulting SafeNet specializes in being partners in your success. We currently focus on Custom Application Development, Cloud Consulting Services, Data & Analytics, and User Experience Strategy.
- 5. Introduction What is Jupyter? What is Azure Notebooks? Intro to the Example Code, Execution & Markdown Data Visualization Where does this tool fit? Question & Answer Agenda
- 7. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter Notebook jupyter.org
- 8. Share Notebooks Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. Interactive Output Your code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types. Big Data Integration Leverage big data tools, such as Apache Spark, from Python, R and Scala. Explore that same data with pandas, scikit-learn, ggplot2, TensorFlow Use Your Language of Choice Jupyter has support for over 40 programming languages, including Python, R, Julia, and Scala.
- 9. Data powers Netflix. It permeates our thoughts, informs our decisions, and challenges our assumptions. It fuels experimentation and innovation at unprecedented scale. Our 100 Petabytes of data helps us discover fantastic content and deliver personalized experiences for our 130 million members around the world. -- Netflix Blog Industry Example
- 10. Jupyter adoption hits critical mass with Netflix Data Scientists. Data Scientist Adoption Data Engineers began work to elevate notebooks from a niche tool to a first-class citizen at Netflix for data consumption. Developers Take Notice All user types rapidly adopt because of the versatility, power, and ease of use Organic Adoption Today Jupyter Notebooks is the most popular platform for data consumption at Netflix. Jupyter Notebooks Data Access at Netflix / Use Case / Source: https://bit.ly/netflixjupyter/
- 11. What is Azure Notebooks?
- 12. Azure Notebooks Useful for any workspace Perfect for data scientists, developers, students, or anyone. Develop and run code in your browser regardless of industry or skillset. Multiple language support Supporting more languages than any other platform including Python 2, Python 3, R, and F#. Microsoft Azure Cloud Services Created by Microsoft Azure: Always accessible, always available from any browser, anywhere in the world. Source: https://notebooks.azure.com/
- 13. What’s the Big Deal? / With Jupyter in Azure / Create an account and you are up and running! Give read only access to anyone via a link or Download a copy of the IPYNB file to share. It is easy to attach an Azure Notebook to a DSVM for more power! Integrate easily with GitHub, Clone to your own Notebook, Cloud based Storage and Backups No Install Needed Share Anywhere Cloud Compute Cloud Features
- 14. Intro to the Example
- 15. The Example This example is presented in Jupyter bringing a human readable quality that is missing in my workshop. Different Angle This example is content borrowed from my full day workshop. Workshop I’m focusing on Jupyter not the code, I will highlight a couple things in code, but we will focus on Azure Notebooks. Don’t Focus on the Code
- 16. Region An ANN that predicts if a sales lead will be a WIN or a LOSS Client Size Route to Market Days in Sales Stage Etc… Sales Prediction / Will you make the sale? / Deal Size
- 17. Code, Execution, and Markdown
- 18. A collection of extensions that add functionality to the Jupyter notebook. These extensions are mostly written in JavaScript and will be loaded locally in your browser. Jupyter Notebook Extensions Source: https://github.com/ipython-contrib/jupyter_contrib_nbextensions
- 19. Collapsible Headings Allows notebook to have collapsible sections, separated by headings. Notify Jupyter notebook extension to display a web notification to notify you when the kernel becomes idle. This can be useful when running tasks that take more than a couple of seconds to complete.
- 20. Lab 2 Let’s Look at Notebooks! TAKE NOTE: • Examine Extensions • Markdown and code mixed • Results in line after the execution • The Data Frame is rendered pretty
- 21. Markdown / Adding the pretty to your Notebooks / • Standard Markdown • Feel free to blend HTML like I did • Check out this cheat sheet: https://www.ibm.com/support/knowledgecenter/SSQNUZ_current/com.i bm.icpdata.doc/dsx/markd-jupyter.html
- 23. Lab 3, 4, 5 More Notebooks! TAKE NOTE: • More pretty rendering • In line Graph using matplotlib • Array rendering not so pretty • In Line Graph using Seaborn
- 24. 01 / MatPlotLib 02 / Seaborn Graphing Libraries / Visualizing data in Jupyter /
- 25. Lab 6 Even More Notebooks! TAKE NOTE: • Training is very long running • Used Matplotlib to plot the training results
- 26. Where Does This Tool Fit?
- 27. Long Running Tasks Jupyter gracefully handles long running tasks, but is not an ideal environment. Data Management Most of your projects will have associated data sets, but where do you manage them and how do you make them portable? Writing Code A lot of our IDE expectations are not met by Jupyter, making it not the best place to write code Weaknesses
- 28. Lab 7 + 8 Don’t Do This! TAKE NOTE: • Jupyter is a poor fit for a grid search • Jupyter renders lab 8 output poorly
- 29. Developers Jupyter give developers a sand box to work in especially when shaping data, and allows them to understand the work of data scientists Data Scientists Jupyter fits the workflow of a data scientist allowing them to codify their work in a presentation style that shares both the how and the why