Welcome to "Hands-on deep learning with TensorFlow 2.0 and Azure" Workshop!

Overview

This repository contains content of a four 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

• Part 4: Explaining Your Model Predictions

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/2OjknZW

• 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:

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• Click Get started!

• In the welcome screen click on Take a quick tour button to familiarize yourself with Azure ML studio.

2. 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.

3. 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/microsoft/bert-stack-overflow.git

4. Open Part 1 of the workshop

• Go back to the Jupyter window.

• Navigate to bert-stack-overflow/1-Training/ folder.

• Open AzureServiceClassifier_Training.ipynb notebook.

You are ready to start your workshop! Have fun.