@ -35,8 +40,55 @@ In the [previous lesson](../18-tbd/README.md), we saw how to train, deploy and c
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### 1.2 Heart failure prediction project and dataset introduction
Check [here](../18-tbd/README.md) the Heart failure prediction project and dataset introduction.
Check [here](../18-tbd/README.md) the Heart failure prediction project and dataset introduction.
## 2. Training a model with the Azure ML SDK
### 2.1 Create an Azure ML workspace
For simplicity, we are going to work on a jupyter notebook. This implies that you already have a Workspace and a compute instance. If you already have a Workspace, you can directly jump to the section 2.3 Notebook creation.
If not, please follow the instructions in the section **2.1 Create an Azure ML workspace** in the [previous lesson](../18-tbd/README.md) to create a workspace.
### 2.2 Create a compute instance
In the [Azure ML workspace](https://ml.azure.com/) that we created earlier, go to the compute menue and you will see the different compute resources available
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Let's create a compute instance to provision a jupyter notebook.
1. Click on the + New button.
2. Give a name to your compute instance.
3. Choose your options: CPU or GPU, VM size and core number.
4. Click in the Create button.
Congratulations, you have just created a compute instance! We will use this compute instance to create a Notebook the [Creating Notebooks section](#creating-notebooks)
### 2.3 Creating Notebooks
Notebook are a really important part of the data science process. They can be used to Conduct Exploratory Data Analysis (EDA), call out to a computer cluster to train a model, call out to an inference cluster to deploy an endpoint.
To create a Notebook, we need a compute node that is serving out the jupyter notebook instance. Go back to the [Azure ML workspace](https://ml.azure.com/) and click on Compute instances. In the list of compute instances you should see the [compute instance we created earlier](#222-creating-compute-resources).
1. In the Applications section, click on the Jupyter option.
2. Tick the "Yes, I understand" box and click on the Continue button.
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3. This should open a new browser tab with you jupyter notebook instance as follow.
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Import the class and create a new workspace by using the following code:
```python
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)
```
## 🚀 Challenge
## 🚀 Challenge
@ -50,3 +102,5 @@ Check [here](../18-tbd/README.md) the Heart failure prediction project and datas