Enterprise Services: Machine Learning Implementation on Microsoft Azure
Machine Learning
| Intermediate
- 14 Videos | 1h 18m 36s
- Includes Assessment
- Earns a Badge
Explore the features and operational benefits of using a cloud platform to implement ML (machine learning) by using Microsoft Azure and Amazon SageMaker, in this 14-video course. First, you will learn how to use Microsoft Azure ML tools, services, and capabilities, and how to examine MLOps (machine learning and operations) to manage, deploy, and monitor models for quality and consistency. You will create Azure Machine Learning workspaces, and learn to configure development environments, build, and manage ML pipelines, to work with data sets, train models, and projects. You will develop and deploy predictive analytic solutions using the Azure Machine Learning Service visual interface, and work with Azure Machine Learning R Notebooks to fit and publish models. You will learn to enable CI/CD (continuous integration and continuous delivery) with Azure Pipelines, and examine ML tools in AWS (Amazon Web Services) SageMaker, and how to use Amazon's ML console. Finally, you will learn to track code from Azure Repos or GitHub, trigger release pipelines, and automate ML deployments by using Azure Pipelines.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
describe Azure machine learning tools, services, and capabilities
compare the capabilities of Azure Machine Learning Studio and Azure Machine Learning Service
create Azure Machine Learning Service workspaces and configure development environments for Azure Machine Learning
build and manage machine learning pipelines with Azure Machine Learning Service
launch the Microsoft Azure Machine Learning Studio and work with datasets, train models, and projects
use the Azure Machine Learning Service visual interface to develop and deploy predictive analytic solutions -
access, transform, and join data using Azure Open Datasets and train automated machine learning regression models to calculate model accuracy
describe the capabilities of MLOps with focus on managing, deploying, and monitoring models using Azure Machine Learning Service to improve the quality and consistency of machine learning solutions
work with Azure Machine Learning R Notebooks to fit models and publish models as web services
build predictive pipelines, incorporating Azure Data Lake and Azure Machine Learning
enable CI/CD for machine learning projects with Azure Pipelines
use the ML extension of Visual Studio from Microsoft DevLabs to track code from Azure Repos or GitHub, trigger release pipelines, and automate machine learning deployments using Azure Pipelines
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1mUP NEXT
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2.Azure Machine Learning Tools and Capabilities8m 48s
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3.Comparing Azure ML Studio and Azure ML Service8m 35s
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4.Creating & Configuring Azure ML Service Workspace3m 36s
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5.Building ML Pipelines with Azure ML Service4m 13s
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6.Working with Azure ML Studio4m 40s
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7.Using Azure ML Service Visual Interface5m 38s
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8.Working with Azure Open Datasets6m 21s
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9.Azure MLOps5m 17s
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10.Azure ML R Notebooks8m 14s
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11.Pipelines with Azure Data Lake and Azure ML6m 2s
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12.CI/CD for Machine Learning with Azure Pipeline5m 11s
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13.Using Microsoft DevLabs Extension3m 47s
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14.Course Summary1m 14s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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