Classifying Iris with Decision Forest Algorithm

Before Getting Started

We need to create accounts for Machine Learning Experimentation and Model Management and install ML Workbench.

Create Machine Learning Experimentation

Azure account recommended (sign up for free trial here if you don’t already have an account).

Option 1:

In the Azure portal, click the "New" button, then “Data + Analytics” and then “Machine Learning Experimentation”.
Fill in the account name.
Choose the subscription that you want billed.

Option 2:

sh az ml account experimentation create -n (account) -g (resourceGroup)

Install Azure ML Workbench

Install Azure Machine Learning Workbench on Windows

Install Azure Machine Learning Workbench on macOS

QuickStart

Clone repository

 git clone https://github.com/jcdesousa/azure-ml-experiment.git

Launch Workbench.
Add Existing Folder as Project
Select local as the execution environment, and iris_sklearn_forest.py as the script, and click Run button.

Exploring results

After running, you can check out the results in Run History. Exploring the Run History will allow you to see the correlation between the parameters you entered and the accuracy of the models. You can get individual run details by clicking a run in the Run History report or clicking the name of the run on the Jobs Panel to the right. In this sample you will have richer results if you have matplotlib installed.

Deploying a Model

Docker Installation

Environment Setup

To start the setup process, you need to register a few environment providers by entering the following commands:

az provider register -n Microsoft.MachineLearningCompute
az provider register -n Microsoft.ContainerRegistry
az provider register -n Microsoft.ContainerService

Local deployment

To deploy and test your web service on the local machine, set up a local environment using the following command. The resource group name is optional.

az ml env setup -l [Azure Region, e.g. eastus2] -n [your environment name] [-g [existing resource group]]

After setup completes successfully, set the environment to be used using the following command:

az ml env set -n [environment name] -g [resource group]

Create a Model Management Account

A model management account is required for deploying models. You need to do this once per subscription, and can reuse the same account in multiple deployments.

To create a new account, use the following command:

az ml account modelmanagement create -l [Azure region, e.g. eastus2] -n [your account name] -g [resource group name] --sku-instances [number of instances, e.g. 1] --sku-name [Pricing tier for example S1]

Storage Account Connection String

To find out which storage account is in use, type:

az ml env show

G o to the Azure Console and click on “Storage accounts”. Find the account that you got back from the command and get the connection string.

Add Storage Account Connection String to Environment

export AML_MODEL_DC_STORAGE="<connection string>"

Create and deploy the web service

Register a model, create a manifest, create an image, as well as, create and deploy the webservice, as one step as follows.

az ml service create realtime -f score_iris.py --model-file model.pkl -s service_schema.json -n irisapp -r -python --collect-model-data true

Test the service

Use the following command to get information on how to call the service:

az ml service usage realtime -i irisapp
az ml service keys realtime -i irisapp

curl -X POST -H "Content-Type:application/json" --data "{\"input_df\": [{\"petal width\": 0.25, \"sepal length\": 3.0, \"sepal width\": 3.6, \"petal length\": 1.3}]}" http://127.0.0.1:32770/score

jcdesousa / azure-ml-experiment