From b0c31e7c8237505aae4c45ebf026e5e135bea8b7 Mon Sep 17 00:00:00 2001 From: Amagash Date: Tue, 31 Aug 2021 18:11:42 +0200 Subject: [PATCH] [Lesson 19] Add Note about notebooks --- 5-Data-Science-In-Cloud/19-tbd/README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/5-Data-Science-In-Cloud/19-tbd/README.md b/5-Data-Science-In-Cloud/19-tbd/README.md index 58fe4f31..45630dbe 100644 --- a/5-Data-Science-In-Cloud/19-tbd/README.md +++ b/5-Data-Science-In-Cloud/19-tbd/README.md @@ -77,6 +77,9 @@ Congratulations, you have just created a compute instance! We will use this comp Refer the [previous lesson](../18-tbd/README.md) in the section **2.3 Loading the Dataset** if you have not uploaded the dataset yet. ### 2.4 Creating Notebooks + +> **_NOTE:_** For the next step you can either create a new notebook from scratch, or you can upload the [notebook we created](notebook.ipynb) in you Azure ML Studio. To upload it, simply click on the "Notebook" menu and upload the notebook. + 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](#22-create-a-compute-instance). @@ -115,7 +118,7 @@ To get or create an experiment from a workspace, you request the experiment usin Now you need to create a compute cluster for the training using the following code. Note that this step can take a few minutes. ```python -from azureml.core.compute import ComputeTarget, AmlCompute +from azureml.core.compute import AmlCompute aml_name = "heart-f-cluster" try: @@ -222,7 +225,7 @@ The model is deployed using the [deploy](https://docs.microsoft.com/en-us/python ```python from azureml.core.model import InferenceConfig, Model -from azureml.core.webservice import AciWebservice, Webservice +from azureml.core.webservice import AciWebservice inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())