Course curriculum

  • 1

    1 - Welcome and Introduction

    • DP-100 Course Welcome

    • Exam Requirements

  • 2

    2 - Create an Azure Machine Learning workspace

    • Create an Azure Machine Learning workspace

    • Azure ML Workspace Settings - Portal

    • Azure ML Studio Settings

  • 3

    3 - Manage data objects in an Azure Machine Learning workspace

    • Datastores and Datasets

    • Create Additional Datasets

  • 4

    4 - Manage experiment compute contexts

    • Create an experiment compute instance

    • Manage multiple compute instances

    • Create compute targets and clusters

  • 5

    5 - Create models by using Azure Machine Learning Designer

    • Creating Our First ML Pipeline

    • Submitting the Pipeline for Training and Evaluation

    • Custom Code in a More Complicated Pipeline

    • Explaining the Complicated Pipeline

    • Evaluating the Execution Results

    • Azure ML Designer Errors

    • Azure ML Designer Modules

  • 6

    6 - Run training scripts in an Azure Machine Learning workspace

    • Setup SDK for Azure ML

    • Create ML Workspace using SDK

    • Hello, World in Python

    • Train a Model using SDK

    • Submit Experiment using SDK

  • 7

    Generate metrics from an experiment run

    • Metrics

  • 8

    7 - Automate the model training process

    • Create a pipeline by using the SDK

  • 9

    8 - Use Automated ML to create optimal models

    • Overview of AutoML

    • Using AutoML with SDK

  • 10

    9 - Use Hyperdrive to tune hyperparameters

    • What is Hyperparameter?

  • 11

    10 - Manage models

    • Register a trained model

  • 12

    11 - Create production compute targets

    • Create production compute targets

  • 13

    12 - Deploy a model as a service

    • Deploy AutoML

    • Create an AutoML Endpoint

    • Deploy ML Designer for Real Time Inference

    • Deploy SDK Models

  • 14

    13 - Create a pipeline for batch inferencing

    • Publish a Pipeline for Batch Inference

  • 15

    14 - Wrap Up

    • Thank You!