Welcome to "Deep Learning for Partner Cloud Architects" Workshop!

Overview

This repository contains content of a three part workshop of using Tensorflow 2.0 on Azure Machine Learning service. The different components of the workshop are as follows:

• Part 1: Preparing Data and Model Training
• Part 2: Inferencing and Deploying a Model
• Part 3: Setting Up a Pipeline Using MLOps

The workshop demonstrates end-to-end Machine Learning workflow on the example of training a BERT model to automatically tag questions on Stack Overflow.

Getting started with the workshop environment

1. Provision your personal Lab environment

   • Open Registration URL: http://bit.ly/2WLjeMZ
   • Enter Activation Code which should be provided by the instructors of the workshop.
   • Fill out registration form and Submit it.
   • On the next screen click Launch Lab.
   • Wait until your personal environment is provisioned. It should take approximatly 3-5 minutes.

2. Login to Azure ML studio

   • Once the workshop enviroment is ready, you can open new browser tab and navigate to Azure ML studio, using it's direct URL: https://ml.azure.com. We recommend to use Private Browser window for the login to avoid conflicting credentials if you already have Azure subscription.
   • Use credentials provided in the workshop environment to sign-in to Azure ML studio.
   • In the Welcome screen select preprovisioned subcription and workspace similar to screenshot below:
   • Click Get started!
   • In the welcome screen click on Take a quick tour button to familiarize yourself with Azure ML studio.

3. Upgrade to Enterprise Edition

   • Azure ML workspace provides advanced capabilities in its Enterprise Edition.
   • To unlock Enterprise Edition click on Automated ML tab in the left navigation bar.
   • Then, in the Upgrade dialog box, click Upgrade.

4. Create Azure Machine Learning Notebook VM

   • Click on Compute tab on the left navigation bar.
   • In the Notebook VM section, click New.
   • Enter Notebook VM name of your choice and click Create. Creation should take approximately 5 minutes.

5. Clone this repository to Notebook VM in your Azure ML workspace

   • Once Notebook VM is created and in Running state, click on the Jupyter link. This will open Jupyter web UI in new browser tab.
   • In Jupyter UI click New > Terminal.
   • In terminal window, type and execute command: ls
   • Notice the name of your user folder and use that name to execute next command: cd <userfolder>
   • Clone the repository of this workshop by executing following command: git clone https://github.com/maxluk/bert-cloud-architect-boot-camp.git

6. Open Part 1 of the workshop

   • Go back to the Jupyter window.
   • Navigate to bert-cloud-architect-boot-camp/1-Training/ folder.
   • Open AzureServiceClassifier_Training.ipynb notebook.

You are ready to start your workshop! Have fun.