Machine Learning Engineer with Microsoft Azure

Welcome to the Machine Learning Engineer with Microsoft Azure Nanodegree program! In this lesson, you will learn more about the structure of the program and meet your instructors.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

In this lesson, we'll look at an overview of the course, look ahead to your final project, and discuss some of the reasons why—and when—you would want to do Machine Learning in the cloud.

In this lesson, we'll learn how workspaces and AzureML studio enable you to be more productive as a data scientist or ML engineer.

Datastores and Datasets are a critical component of cloud computing. We'll learn how to integrate third party datasets and open datasets into our ML pipeline to quickly develop working solutions.

We'll learn how to manage pipelines and use hyperparameters in experiments, as well as how to automate changes that create huge value in terms of prediction accuracy.

In this lesson, we'll cover how the Azure ML SDK allows us to programmatically create and manage pipelines. We'll see that this approach makes pipeline creation and management a reproducible process.

We'll discuss how we can use the Azure ML SDK to do automated ML and create models more quickly and accurately. We'll also have a look at how we can make we can make our models easier to interpret.

This is a welcome lesson. In this lesson, you will get an overview of the course

In this lesson, you will learn about how to authorize operations for machine learning and deploy machine learning models to Azure.

In this lesson, you will learn about how to consume deployed service and load-test the endpoint.

In this lesson, you will learn how to create a batch inference pipeline, publish a pipeline and consume the pipeline endpoint.