DP-100 Part 3 - Deployment and Working with SDK

By Brian Roehm

In this course, we focus on how to manage and run experiments in Azure Machine Learning using Azure Machine Learning SDK.

9 hours
  • 45 Lessons
  • 5 Hands-On Labs
  • 1 Practice Exam

About the course

In this course, we focus on how to manage and run experiments in Azure Machine Learning using Azure Machine Learning SDK. This course is part three of a three-part series, focusing on preparation for the DP-100 exam.

This course will also include a review of course parts 1 and 2 as well as a comprehensive practice exam to help prepare you for the DP-100 exam.

  • Chapter 1 7 Lessons Introduction 19:40

    Course Introduction

    1:02

    About the Training Architect

    1:12

    About the Exam

    6:35

    A Note on Data Science and Mathematics

    1:37

    A Note on Python

    2:09

    Azure Machine Learning SDK

    4:23

    Path to Certification

    2:42
  • Chapter 2 4 Lessons Running Training Scripts in an Azure Machine Learning Workspace 24:57

    Workspace and Compute Preparation

    8:57

    Configuring Run Settings for a Script

    7:17

    Consuming Data from a Dataset in an Experiment by Using SDK

    5:08

    Section Review

    3:35
  • Chapter 3 3 Lessons Automating the Model Training Process 9:49

    Creating a Pipeline by Using the SDK: Part 1

    6:06

    Creating a Pipeline by Using the SDK: Part 2

    3:43

    Create a Pipeline in Azure Machine Learning using SDK

    0:00 Hands-On Lab
  • Chapter 4 4 Lessons Using Automated ML to Create Optimal Models 16:54

    Using Automated ML Interface in Azure Machine Learning Studio

    7:59

    Preprocessing Options

    6:09

    Section Review

    2:46

    Use Automated ML from the Azure Machine Learning SDK

    0:00 Hands-On Lab
  • Chapter 5 6 Lessons Using Hyperdrive to Tune Hyperparameters 25:03

    What Is a Hyperdrive and Can I Use It to Get to the Future?

    5:42

    Tuning Hyperparameters for Peak Performance

    5:19

    Selecting a Sampling Method

    4:31

    Smokey and the Bandit and All His Rowdy Friends

    3:45

    Finding the Model That Has Optimal Hyperparameter Values

    5:46

    Exploring Hyperparameters in Azure Machine Learning

    0:00 Hands-On Lab
  • Chapter 6 3 Lessons Using Model Explainers to Interpret Models 17:43

    Model Interpretability and Explainers in Azure

    7:06

    Model Interpretability in Python

    7:10

    Section Review

    3:27
  • Chapter 7 4 Lessons Managing Models 17:53

    Registering a Trained Model

    3:55

    Monitoring Model Usage with Azure Monitor

    3:54

    Monitoring Data Drift

    10:04

    Monitoring the Environment in Azure Machine Learning

    0:00 Hands-On Lab
  • Chapter 8 4 Lessons Deploying a Model as a Service 18:31

    Evaluating Deployment Options

    8:06

    Considering Security for Deployed Services

    2:18

    Troubleshooting Container Issues

    4:56

    Section Review

    3:11
  • Chapter 9 2 Lessons Creating a Pipeline for Batch Inferencing 5:01

    Publishing a Batch Inferencing Pipeline

    5:01

    Run a Batch Inferencing Pipeline and Obtain Outputs

    0:00 Hands-On Lab
  • Chapter 10 3 Lessons Part 1 Review 13:25

    Azure Machine Learning Workspace Creation

    4:51

    Azure Machine Learning Data Management

    3:16

    Azure Machine Learning Compute

    5:18
  • Chapter 11 3 Lessons Part 2 Review 15:16

    Machine Learning Algorithms You Need to Know

    6:06

    Feature Selection: Some Tools and Concepts You Need to Know

    3:49

    Classic Machine Learning Models

    5:21
  • Chapter 12 3 Lessons Conclusion 1:06:36

    Thanks and What's Next

    1:14

    Exam Tips

    5:22

    DP-100 Practice Exam

    1:00:00 Quiz

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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