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.
Enterprise Services: Machine Learning Implementation on Microsoft Azure
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