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IT Professional Curricula Internet and Network Technologies Solution Area Cloud Computing AI-900: Azure AI Fundamentals

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.

Asset ID	it_clazaif_03_enus
Course Type	Video Course
Course Category	Internet and Network Technologies Solution Area

Objectives
AI-900: Azure AI Fundamentals: Using Azure Machine Learning Studio 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

Objectives

AI-900: Azure AI Fundamentals: Using Azure Machine Learning Studio

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