Enterprise Services: Machine Learning Implementation on Microsoft Azure

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 andá 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.