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

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

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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, youll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. Youll 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. Youll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio.

Youll 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 models hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, youll learn to operationalize it for batch or real-time inferences and monitor it in production.

By the end of this Azure certification study guide, youll 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.

Table of Contents
  1. An Overview of Modern Data Science
  2. Deploying Azure Machine Learning Workspace Resources
  3. Azure Machine Learning Studio Components
  4. Configuring the Workspace
  5. Letting the Machines Do the Model Training
  6. Visual Model Training and Publishing
  7. The AzureML Python SDK
  8. Experimenting with Python Code
  9. Optimizing the ML Model
  10. Understanding Model Results
  11. Working with Pipelines
  12. Operationalizing Models with Code

Andreas Botsikas: author's other books


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Azure Data Scientist Associate Certification Guide A hands-on guide to machine - photo 1
Azure Data Scientist Associate Certification Guide

A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

Andreas Botsikas, Ph.D.

Michael Hlobil

BIRMINGHAMMUMBAI Azure Data Scientist Associate Certification Guide Copyright - photo 2

BIRMINGHAMMUMBAI

Azure Data Scientist Associate Certification Guide

Copyright 2021 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Publishing Product Manager: Aditi Gour

Senior Editor: David Sugarman

Content Development Editor: Nathanya Dias

Technical Editor: Sonam Pandey

Copy Editor: Safis Editing

Project Coordinator: Aparna Ravikumar Nair

Proofreader: Safis Editing

Indexer: Tejal Daruwale Soni

Production Designer: Vijay Kamble

First published: December 2021

Production reference: 1291021

Published by Packt Publishing Ltd.

Livery Place

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B3 2PB, UK.

ISBN 978-1-80056-500-5

www.packt.com

Contributors
About the authors

Andreas Botsikas is an experienced advisor working in the software industry. He has worked in the finance sector, leading highly efficient DevOps teams, and architecting and building high-volume transactional systems. He then traveled the world, building AI-infused solutions with a group of engineers and data scientists. Currently, he works as a trusted advisor for customers onboarding into Azure, de-risking and accelerating their cloud journey. He is a strong engineering professional with a Doctor of Philosophy (Ph.D.) in resource optimization with artificial intelligence from the National Technical University of Athens.

I want to thank the people who have been close to me and supported me, especially my family. Moreover, I would like to thank the Packt team and the reviewers for the tremendous support throughout the book authoring process. Finally, I would like to thank Michael, who challenged me to co-write this book. The journey was long, full of adventures, full of discoveries, exactly as I was hoping it to be.

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Michael Hlobil is an experienced architect focused on quickly understanding customers' business needs, with over 25 years of experience in IT pitfalls and successful projects, and is dedicated to creating solutions based on the Microsoft Platform. He has an MBA in Computer Science and Economics (from the Technical University and the University of Vienna) and an MSc (from the ESBA) in Systemic Coaching. He was working on advanced analytics projects in the last decade, including massive parallel systems and Machine Learning systems. He enjoys working with customers and supporting the journey to the cloud.

I would like to first and foremost thank my grandparents: Oma and Opa Danke fr eure unermdliche Untersttzung ohne euch wre ich nicht das geworden was ich bin!. Moreover, I would like to thank my family. My wife and my three kids: Danke fr alles was ich lernen durfte und lernen darf!. A huge thank you to Andreas: " !". Last but not least to the Packt team and the reviewers for the tremendous work they did throughout the book authoring process.

I have known Andreas and Michael from our FastTrack for Azure organization. Our core strategy is to meet customers wherever they are in their current cloud journey primarily to accelerate and de-risk cloud deployments at scale while improving the Azure platform. Both are enabling customers all over the world to become successful with their Artificial Intelligence projects. AI plays a crucial role in the digital revolution we experience every day. There are challenges with every new technology, especially when it comes to building trust in the latest technology. This book covers both how to produce AI but also covers aspects of building responsible AI. I think these two aspects makes this book so valuable. I hope you enjoy it.

Juergen Daiberl, General Manager - FastTrack for Azure Lead

The exponential growth of data and the rapid evolution of cloud computing have put topics such as data science and machine learning on top of the agenda for many organizations. Andreas and Michaels long experience with hundreds of our customers comes together in this book, combining the concepts of AI with practical guidance on how to use it in real life scenarios, using the Microsoft Azure cloud. The insights and knowledge they share will help you find the right opportunities for AI in your organization and enable you to get started. Im inspired by Andreas and Michaels work and how they live Microsofts mission to empower every person and every organization on the planet to achieve more.

Ricardo Henriques, EMEA Lead - FastTrack for Azure

About the reviewers

Arianna Olivelli is a marine scientist based in London. She is currently undertaking a Ph.D. in chemical oceanography. In the past, she has worked on numerical, statistical, and machine learning models focused on marine and riverine plastic pollution. In her free time, she loves to play sports and spend time outdoors.

Maria Vrabie is an engineer working in the FastTrack for Azure team within the Engineering organization at Microsoft. She is currently helping various businesses implement Azure solutions focusing on Intelligent agents, Cognitive Services, as well as analytics. Community enthusiast and inclusion advocate, she has been volunteering at different events throughout years. In her spare time, she enjoys cycling and Latin dancing.

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