Azure Data Scientist Associate Certification Guide: A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

by Andreas Botsikas, Michael Hlobil

Length: 448 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-12-03
ISBN-10: 1800565003
ISBN-13: 9781800565005
Sales Rank: #1450040 (See Top 100 Books)

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Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease

Key Features

Create end-to-end machine learning training pipelines, with or without code
Track experiment progress using the cloud-based MLflow-compatible process of Azure ML services
Operationalize your machine learning models by creating batch and real-time endpoints

Book Description

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate.

Starting with an introduction to data science, you’ll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You’ll discover the studio interface and manage various components, such as data stores and compute clusters.

Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You’ll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio.

You’ll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model’s hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you’ll learn to operationalize it for batch or real-time inferences and monitor it in production.

By the end of this Azure certification study guide, you’ll have gained the knowledge and the practical skills required to pass the DP-100 exam.

What you will learn

Create a working environment for data science workloads on Azure
Run data experiments using Azure Machine Learning services
Create training and inference pipelines using the designer or code
Discover the best model for your dataset using Automated ML
Use hyperparameter tuning to optimize trained models
Deploy, use, and monitor models in production
Interpret the predictions of a trained model

Who this book is for

This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.

Azure Data Scientist Associate Certification Guide
Contributors
About the authors
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Share Your Thoughts
Section 1: Starting your cloud-based data science journey 
Chapter 1: An Overview of Modern Data Science
    The evolution of data science
    Working on a data science project
        Understanding of the business problem
        Acquiring and exploring the data
        Feature engineering
        Training the model
        Deploying the model
    Using Spark in data science
    Adopting the DevOps mindset
    Summary
    Further reading
Chapter 2: Deploying Azure Machine Learning Workspace Resources
    Technical requirements
    Deploying Azure ML through the portal
        Using the deployment wizard
    Deploying Azure ML via the CLI
        Deploying Azure Cloud Shell
        Using the Azure CLI
        Installing the Azure ML CLI extension
        Deploying Azure ML using the az ml command
        Cleaning up the CLI resource group
    Alternative ways to deploy an Azure ML workspace
    Exploring the deployed Azure resources
        Understanding Role-Based Access Control (RBAC)
        RBAC inheritance
        Creating custom roles
        Assigning roles in the Azure ML workspace
    Summary
    Questions
    Further reading
Chapter 3: Azure Machine Learning Studio Components
    Technical requirements
    Interacting with the Azure ML resource
    Exploring the Azure ML Studio experience
    Authoring experiments within Azure ML Studio
    Tracking data science assets in Azure ML Studio
    Managing infrastructure resources in Azure ML Studio
    Summary
Chapter 4: Configuring the Workspace
    Technical requirements
    Provisioning compute resources
        Compute instances
        Compute clusters
        Inference clusters
        Attached compute
    Connecting to datastores
        Types of datastores
        Datastore security considerations
    Working with datasets
        Registering datasets
        Exploring the dataset
        Data drift detection
    Summary
    Questions
    Further reading
Section 2: No code data science experimentation
Chapter 5: Letting the Machines Do the Model Training
    Technical requirements
    Configuring an AutoML experiment
        Registering the dataset
        Returning to the AutoML wizard
    Monitoring the execution of the experiment
    Deploying the best model as a web service
        Understanding the deployment of the model
        Cleaning up the model deployment
    Summary
    Question
    Further reading
Chapter 6: Visual Model Training and Publishing
    Technical requirements
    Overview of the designer
        The authoring screen/view
        Understanding the asset library
        Exploring the asset's inputs and outputs
    Building the pipeline with the designer
        Acquiring the data
        Preparing the data and training the model
        Executing the training pipeline
    Creating a batch and real-time inference pipeline
        Creating a batch pipeline
        Creating a real-time pipeline
    Deploying a real-time inference pipeline
    Summary
    Question
    Further reading
Section 3: Advanced data science tooling and capabilities  
Chapter 7: The AzureML Python SDK
    Technical requirements
    Overview of the Python SDK
    Working in AzureML notebooks
    Basic coding with the AzureML SDK
        Authenticating from your device
        Working with compute targets
        Defining datastores
        Working with datasets
    Working with the AzureML CLI extension
    Summary
    Questions
    Further reading
Chapter 8: Experimenting with Python Code
    Technical requirements
    Training a simple sklearn model within notebooks
    Tracking metrics in Experiments
        Tracking model evolution
        Using MLflow to track Experiments
    Scaling the training process with compute clusters
        Exploring the outputs and logs of a run
        Understanding execution environments
        Training the diabetes model on a compute cluster
        Utilizing more than a single compute node during model training
    Summary
    Questions
    Further reading
Chapter 9: Optimizing the ML Model
    Technical requirements
    Hyperparameter tuning using HyperDrive
        Using the early termination policy
    Running AutoML experiments with code
    Summary
    Questions
    Further reading
Chapter 10: Understanding Model Results
    Technical requirements
    Creating responsible machine learning models
    Interpreting the predictions of the model
        Training a loans approval model
        Using the tabular explainer
        Understanding the tabular data interpretation techniques
        Reviewing the interpretation results
    Analyzing model errors
    Detecting potential model fairness issues
    Summary
    Questions
    Further reading
Chapter 11: Working with Pipelines
    Technical requirements
    Understanding AzureML pipelines
    Authoring a pipeline
        Troubleshooting code issues
    Publishing a pipeline to expose it as an endpoint
    Scheduling a recurring pipeline
    Summary
    Questions
    Further reading
Chapter 12: Operationalizing Models with Code
    Technical requirements
    Understanding the various deployment options
    Registering models in the workspace
    Deploying real-time endpoints
        Understanding the model deployment options
        Profiling the model's resource requirements
        Monitoring with Application Insights
        Integrating with third-party applications
    Creating a batch inference pipeline
    Summary
    Questions
    Further reading
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