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.