Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure

By Tim Warner
This course is for data pros, developers, and IT pros with different areas of responsibility who need to collaborate effectively on data science projects and iteratively develop repeatable, high-quality machine learning models in Microsoft Azure.
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Course info
Rating
(10)
Level
Beginner
Updated
Dec 10, 2019
Duration
2h 30m
Table of contents
Course Overview
Course Overview 1m
Understanding Machine Learning Workspaces
Summary 1m Preliminary Definitions 2m How You Interact with the Azure ML Workspace 1m Demo: Touring the Azure Machine Learning Workspace 4m The Azure Machine Learning Workspace 4m Overview 1m The Azure Machine Learning Landscape 5m Demo: Touring Azure Machine Learning Studio 9m
Understanding Azure ML Pipelines
The Azure Machine Learning Pipeline 5m Preliminary Definitions 3m Demo: Run and Publish the ML Pipeline 6m Planning an Azure ML Pipeline 1m Publishing an Azure ML Pipeline 1m Summary 1m Overview 1m Demo: Build an Azure ML Pipeline 10m
Managing Machine Learning Workspaces
Overview 1m Summary 2m Demo: Working with Jupyter Notebooks 4m Demo: Getting Familiar with the Workspace 3m Demo: Secure Azure ML with RBAC 2m Create a Workspace 2m Demo: Deploy an Azure ML Workspace 5m Role-Based Access Control (RBAC) in Azure 2m Demo: Create and Test a Custom RBAC Role 6m Understand the Azure Machine Learning Workspace 2m
Implementing AI Pipelines
Summary 1m Demo: Run an Experiment with the Python SDK 8m Demo: Deploy an Azure Data Science VM 6m Demo: Set up Our Workspace Environment 6m Overview 1m The Azure Data Science Virtual Machine 3m The Azure ML Pipeline Deployment Process 3m
Managing Experiments
Summary 1m Demo: Prepare a Dataset 2m Demo: Deploy the Best ML Model 2m Demo: Create an Automated ML Experiment 2m Automated Machine Learning 2m Overview 1m Demo: Prepare a Datastore 4m Datastore vs. Dataset 5m
Managing Data Flow and Logging
Overview 1m Demo: Plotting Azure ML Resource Metrics and Raising Alerts 4m Summary 2m Demo: Using the Azure ML Pyhon SDK to Monitor Model Training 3m Demo: Monitoring Experiments in Azure Machine Learning Studio 2m Demo: Querying Azure ML Log Data 3m Monitoring the Azure Machine Learning Service 1m Monitoring the ML Model Training Process 1m
Description
Course info
Rating
(10)
Level
Beginner
Updated
Dec 10, 2019
Duration
2h 30m
Description

At the core of being a Microsoft Azure AI engineer rests the need for effective collaboration. In this course, Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure, you will learn how to develop, deploy, and monitor repeatable, high-quality machine learning models with the Microsoft Azure Machine Learning service. First, you will understand how to create no-code machine learning pipelines using the Azure ML service visual designer. Next, you will explore how to train ML models using Python, Jupyter notebooks, and the Microsoft Azure Machine Learning workspace. Finally, you will discover how to monitor your Azure Machine Learning environments from the perspective of the data scientist and data engineer. When you are finished with this course, you will have a foundational knowledge of the Microsoft Azure Machine Learning service that will help you as you move forward in the Microsoft Azure AI engineer job role.

About the author
Tim Warner
About the author

Timothy Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management who is based in Nashville, TN.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone, my name is Tim Warner. Welcome to my course, Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure. I'm a Pluralsight staff author, Microsoft MVP, and Microsoft Azure Solutions Architect This beginner‑level course is for data scientists, developers, and IT professionals who need to be able to collaborate effectively on data science projects, as well as to iteratively develop, deploy, and monitor repeatable, high‑quality machine learning models in Microsoft Azure. By the end of the course, you'll understand how to use Azure Machine Learning tools to perform automated, as well as manual machine learning experiments. You'll also learn how to enforce least privilege security and monitor consumption and performance. I hope you'll join me on this journey to master the Azure Machine Learning service in our Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure course, at Pluralsight.

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