From d8b665cad349fb121f2fab32bf25687a0dd50069 Mon Sep 17 00:00:00 2001 From: Amagash Date: Mon, 30 Aug 2021 16:16:53 +0200 Subject: [PATCH] [Lesson 18] Add Compute resource part --- 5-Data-Science-In-Cloud/18-tbd/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/5-Data-Science-In-Cloud/18-tbd/README.md b/5-Data-Science-In-Cloud/18-tbd/README.md index b3bd1a53..d74017e4 100644 --- a/5-Data-Science-In-Cloud/18-tbd/README.md +++ b/5-Data-Science-In-Cloud/18-tbd/README.md @@ -135,6 +135,15 @@ To use Azure Machine Learning, create a workspace in your Azure subscription. Yo You can manage your workspace using the Azure portal, but for data scientists and Machine Learning operations engineers, Azure Machine Learning studio provides a more focused user interface for managing workspace resources. ### 2.2 Compute Resources + +Compute Resources are cloud-based resources on which you can run model training and data exploration processes. There are four kinds of compute resource you can create: + +- **Compute Instances**: Development workstations that data scientists can use to work with data and models. This involves the creation of a Virtual Machine (VM) and launch a notebook instance. You can then train a model by calling a computer cluster from the notebook. +- **Compute Clusters**: Scalable clusters of VMs for on-demand processing of experiment code. You will need it when training a model. Compute clusters can also employ specialized GPU or CPU resources. +- **Inference Clusters**: Deployment targets for predictive services that use your trained models. +- **Attached Compute**: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters. + + #### 2.2.1 Choosing the right options for your compute resources #### 2.2.2 Creating a compute cluster ### 2.3 Loading the Dataset