IT Skills     Cloud Computing and Virtualization     Microsoft     DP-100: Designing and Implementing a Data Science Solution on Azure

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Azure Machine Learning workspaces provide an environment for performing experiments and managing data, computer targets, and other assets. Other assets can include notebooks, pipelines, and trained models. This course will focus on using the Azure Machine Learning SDK.
In this course, you'll learn to create an Azure Machine Learning workspace by creating a machine learning resources, creating compute resources, and cloning a notebook. Next, you'll examine how to install the Machine Learning SDK for Python and create code to connect to a workspace. You'll learn to create Python scripts to run an experiment, log metrics, and retrieve and view logged metrics. Finally, you'll examine how to use the Azure Machine Learning SDK to run code experiments, create a script to train a model, and run a notebook using Jupyter to train predictive models.
This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

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Objectives

DP-100 - Azure Data Scientist Associate: Azure Machine Learning Workspaces discover the key concepts covered in this course describe the features and components of the Azure Machine Learning workspace create an Azure Machine Learning workspace and resource group using Azure Portal and use Azure Machine Learning Studio to create a compute resource, and clone a notebook install the Machine Learning SDK for Python and create code to connect to a workspace create Python scripts to run an experiment, log metrics, and retrieve and view logged metrics use the Azure Machine Learning SDK to run code experiments that log metrics and generate outputs create a script to train a model, add parameters to the script, and run the object to get the training model run a notebook using Jupyter to train predictive models summarize the key concepts covered in this course