One of the most overlooked processes in data science is managing the life cycle of models.
In this course, Deploying and Managing Models in Microsoft Azure, you'll gain foundational knowledge of Azure Machine Learning.
First, you'll discover how to create and utilize Azure Machine Learning.
Next, you'll find out how to integrate with Azure DevOps.
Finally, you'll explore how to utilize them together to automate the deployment and management of models. When you're finished with this course, you'll have the skills and knowledge of model life cycle management needed to manage a machine learning project. Software required: Microsoft Azure.
Course Overview Hi, everyone. My name is Jared Rhodes, and welcome to my course, Deploying and Managing Models in Microsoft Azure. I'm a Microsoft MVP for Azure at QiMata Technologies, a consulting and training firm based out of Atlanta, Georgia. The goal of this course is to get you started deploying and managing machine learning models with Microsoft Azure. In this course, we are going to take a look at Azure machine learning covering the tools, definitions, features, and demo getting started with Azure machine learning. We're going to discuss versioning models, data drift, continuous deployment, and machine learning operations with Azure machine learning. We're also going to go over Application Insights, evaluating an exporting data, root cause analysis, drift metrics, versioning and automation, and the tools available to implement them in Azure. By the end of this course, you'll know the concepts of deploying and managing models and the tools available to you in Microsoft Azure. Before beginning the course, you should be familiar with the Azure portal and somewhat familiar with Python. I hope you'll join me on this journey to learn how to deploy and manage models in the Deploying and Managing Models course at Pluralsight.