Azure AI Fundamentals: Using Azure Machine Learning Studio
Azure 2020
| Beginner
- 18 Videos | 1h 29m 46s
- Includes Assessment
- Earns a Badge
The Azure Machine Learning Studio is a complete web tool and graphical user interface for building, managing, deploying, evaluating, and testing machine learning algorithms and workloads from initial design to final deployment. In this course, you'll investigate the different features of the Azure ML Studio interface and use it to create datasets, ingest data, create models automatically, build prediction services, and finally, manage endpoints for a machine learning model. Furthermore, you'll explore the datastores, compute resources, experiments, pipelines, and model management interfaces that are utilized when working with Azure ML Studio. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
create and configure an Azure Machine Learning workspace
create and use a compute resource using Azure ML Studio
create and use a dataset in Azure ML Studio
ingest data from an Azure Storage source
ingest data from an Azure Blob storage resource
label data within a dataset in the Azure ML Studio interface
identify how to run test scripts manually using Notebook
use the automated ML model to create an experiment that will automatically find the best-fit model -
run an automated ML model experiment to find the best-fit model
evaluate the results of an automated ML model experiment to investigate the best model results
deploy an automated ML model as a predictive service
test an automated ML predictive service by using it to get predictions based on test data
manage and manipulate compute resources and datastores from the Azure ML Studio
manipulate and configure datasets and experiments, including for other team members, in Azure ML Studio
manage stored pipelines and models in Azure ML Studio
manage and configure endpoints in Azure ML Studio
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview56sUP NEXT
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2.Creating an Azure ML Workspace5m 4s
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3.Creating a Compute Resource Using Azure ML Studio5m 11s
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4.Creating and Using a Dataset in Azure ML Studio4m 33s
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5.Ingesting Data from Azure Storage7m 46s
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6.Ingesting Data from Azure Blob Storage6m 57s
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7.Labeling Data in Azure ML Studio8m 10s
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8.Running and Testing Scripts Using Notebook5m 34s
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9.Creating an Automated ML Model5m 25s
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10.Running an Automated Experiment4m 32s
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11.Determining the Best Model after an Experiment4m 5s
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12.Deploying a Model as a Predictive Service3m 9s
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13.Testing the Predictive Service5m 51s
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14.Managing Compute Resources and Datastores4m 49s
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15.Managing Datasets and Experiments in Azure ML Studio5m 23s
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16.Managing Pipelines and Models in Azure ML Studio6m 36s
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17.Managing Endpoints in Azure ML Studio4m 49s
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18.Course Summary56s
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