If you have any questions please contact me at: Mail: [email protected] Twitter: @AxelSirota Linkedin: Axel Sirota
To make the best use of class time, complete the following instructions ahead of class.
IMPORTANT NOTE: There will be some Azure fees incurred if you choose to go through the course exercises. Please read the instructions very carefully.
-
Clone the course GitHub repo locally
- Clone https://github.com/axel-sirota/azure-automl-olt to your local machine.
-
Create an Azure account
- Go to https://azure.microsoft.com/en-us/free/search/ and click Start free .
- You will need to login to your microsoft account (or create one)
- You’ll need to provide a credit card to sign up.
- Select the Free Subscription.
- Go to https://portal.azure.com and login.
-
Create an ML workspace
- On the top Search Bar type:
Machine Learning
and click the resource saying Machine Learning with an erlenmeyer icon - Click on Create Machine Learning workspace
- Under Resource Group click create new and type a name. For example,
auto-ml-olt
. - On workspace name type something unique. For example,
auto-ml-olt-0001
(use random digits) - Click Review and Create and further on, click Create
- On the top Search Bar type:
Now click on Go to Resource and later on, Launch Studio.
-
Create the Compute instance for the notebooks.
- On the left panel of the Studio, click on Compute > New
- Go with the recommended options and click Next
- Type a name, for example,
auto-ml-instance001
(use random digits for it to be unique) - Click on Create, and wait for 5-10 minutes.
- Once the instance is Running, click on it and Stop it. This is extremely important such that it only runs during the training and you are not being overcharged.
-
Create the compute cluster for the training
- On the top panel, next to Compute Instance, you will find and click Compute Cluster.
- Click on New > Next
- Type a cluster name like:
automl-cluster
- Set the maximum number of nodes to 2
- Click on Create
You are ready for the course!!!
Estimated cost for the training: 5 hours * ( 0.29 USD/hour * 3 instances ) = USD 4.40