Azure Machine Learning Engineering

,Table of Contents
Preface
Part 1: Training and Tuning Models with the Azure Machine
Learning Service
1
Introducing the Azure Machine Learning Service
Technical requirements

Building your first AMLS workspace

Creating an AMLS workspace through the Azure portal

Creating an AMLS workspace through the Azure CLI

Creating an AMLS workspace with ARM templates

Navigating AMLS

Creating a compute for writing code

Creating a compute instance through the AMLS GUI

Adding a schedule to a compute instance

Creating a compute instance through the Azure CLI

Creating a compute instance with ARM templates

Developing within AMLS

Developing Python code with Jupyter Notebook

Developing using an AML notebook

Connecting AMLS to VS Code

,Summary

2
Working with Data in AMLS
Technical requirements

Azure Machine Learning datastore overview

Default datastore review

Creating a blob storage account datastore

Creating a blob storage account datastore through Azure Machine Learning
Studio

Creating a blob storage account datastore through the Python SDK

Creating a blob storage account datastore through the Azure Machine Learning
CLI

Creating Azure Machine Learning data assets

Creating a data asset using the UI

Creating a data asset using the Python SDK

Using Azure Machine Learning datasets

Read data in a job

Summary

3
Training Machine Learning Models in AMLS
Technical requirements

Training code-free models with the designer

, Creating a dataset using the user interface

Training on a compute instance

Training on a compute cluster

Summary

4
Tuning Your Models with AMLS
Technical requirements

Understanding model parameters

Sampling hyperparameters

Understanding sweep jobs

Truncation policies

Median policies

Bandit policies

Setting up a sweep job with grid sampling

Setting up a sweep job for random sampling

Setting up a sweep job for Bayesian sampling

Reviewing results of a sweep job

Summary

5
Azure Automated Machine Learning
Technical requirements