The aim of this all-day workshop is to familiarize you with the Microsoft Azure public cloud and some of its Big Data and Machine Learning technologies.

By the time you leave the workshop, you should know the following:

• How to navigate Azure and how to spin up new resources to do your work
• Know different data collection & sanitation techniques and technologies
• Have a feel for implementation of machine learning platforms that will facilitate your research aspirations
• How to have fun working with scaled, (almost) unlimited cloud resources

• 0830-0930: This session gives a quick overview of Azure, Azure Cloud Shell, ML and HPC Azure offerings and how we will interacting with the different technologies of the presented in the day.

  • Intro to Azure Lab: The Azure Portal and Azure Active Directory Groups
  • https://aka.ms/AA4q68y

• 0930-1030: Azure Data Factory is Microsoft's data orchestration and integration tool that you can use to manage, modify and sanitize data before, during or after analyzing it with AI/ML tools.
  • Due to complexity, we'll be walking through a live demo in class and talking through features that will help us "sanitize the data" before we analyze it.
  • https://aka.ms/UNCMLDayADF

• 1045-1145: Our second lab will analyze and sanitize a large amount of data in our friendly, low-code ML Studio UX. Our objective is to visualize this large dataset, sanitize it and then infer predictions based on the previously collected data using a ML algorithm.

  • Azure ML Studio Hands-On Lab

• 1230-1330: This demonstration focuses the new Azure Machine Learning consolidated service that provides SDKs and services to quickly prep data, train, and deploy machine learning models. Improve productivity and costs with auto-scaling compute & pipelines. Use these capabilities with open-source Python frameworks, such as PyTorch, TensorFlow, and scikit-learn.

  • LearnAI Bootcamp

  • AISchool

• 1330-1430: This lab will setup a Databricks workspace. Databricks a hosted Apache Spark environment that is simplified so that minimal infrastructure knowledge is needed to leverage the platform in order to employ Spark Machine Learning or Big Data notebooks.

  • Databricks Setup and Lab 1 Hands-On Lab
  • Exercise 1 and Exercise 2

• 1445-1600: In Exercise 3, we'll use the opensource TensorFlow ML library to analyze the data even further by using deploying a simple Deep Neural Network which will classify claims data.

  • If we have time, Exercise 4 leverages pre-built, compiled and inexpensive public Azure services to analyze the text with Microsoft's Text Analytics API which is part of the Cognitive Services toolkit. These services can be leverage in ANY code, anywhere securely so long as the code has access to the internet.

  • Azure Databricks + TensorFlow Hands-On Lab

  • Exercise 3 & 4 (if you have time)

Thanks for spending time with Microsoft today. We're ALWAYS thrilled to come and speak about our technology and we'd love to hear back from you! Please see below for our contact information and survey.

• Email: [email protected]
• Twitter: http://twitter.com/kfprugger
• LinkedIn: https://www.linkedin.com/in/joeybrakefield/
• GitHub: https://github.com/kfprugger

• Email: [email protected]
• Twitter: https://twitter.com/pauldotyu
• LinkedIn: https://www.linkedin.com/in/yupaul/
• GitHub: https://github.com/pauldotyu

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