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Although this book bears my name as author, many other people contributed to its creation. Without their help, this book wouldn't exist, or at best would exist in a lesser form. Pratap Ramamurthy as my co‐author has helped contribute a third of the content of this book. Kim Wimpsett, the development editor, Christine O'Connor, the managing editor, and Saravanan Dakshinamurthy, the production specialist, oversaw the book as it progressed through all its stages. Arielle Guy was the book's proofreader and Judy Flynn was the copyeditor. Last but not the least, thanks to Hitesh Hinduja for being an amazing reviewer throughout the book writing process.
I'd also like to thank Jim Minatel and Melissa Burlock at Wiley, and Dan Sullivan, who helped connect me with Wiley to write this book.
—Mona Mona
This book is the product of hard work by many people, and it was wonderful to see everyone come together as a team, starting with Jim Minatel and Melissa Burlock from Wiley and including Kim Wimpsett, Christine O' Connor, Saravanan Dakshinamurthy, Judy Flynn, Arielle Guy, and the reviewers.
Most importantly, I would like to thank Mona for spearheading this huge effort. Her knowledge from her previous writing experience and leadership from start to finish was crucial to bringing this book to completion.
—Pratap Ramamurthy
About the Author
Mona Mona is an AI/ML specialist at Google Public Sector. She is the author of the book Natural Language Processing with AWS AI Services and a speaker. She was a senior AI/ML specialist Solution Architect at AWS before joining Google. She has 14 certifications and has created courses for AWS AI/ML Certification Specialty Exam readiness. She has authored 17 blogs on AI/ML and also co‐authored a research paper on AWS CORD‐19 Search: A neural search engine for COVID‐19 literature, which won an award at the Association for the Advancement of Artificial Intelligence (AAAI) conference. She can be reached at monasheetal3@gmail.com.
Pratap Ramamurthy loves to solve problems using machine learning. Currently he is an AI/ML specialist at Google Public Sector. Previously he worked at AWS as a partner solution architect where he helped build the partner ecosystem for Amazon SageMaker. Later he was a principal solution architect at H2O.ai, a company that works on machine learning algorithms for structured data and natural language. Prior to that he was a developer and a researcher. To his credit he has several research papers in networking, server profiling technology, genetic algorithms, and optoelectronics. He holds three patents related to cloud technologies. In his spare time, he likes to teach AI using modern board games. He can be reached at pratap.ram@gmail.com.
About the Technical Editors
Hitesh Hinduja is an ardent artificial intelligence (AI) and data platforms enthusiast currently working as a senior manager in Azure Data and AI at Microsoft. He worked as a senior manager in AI at Ola Electric, where he led a team of 30+ people in the areas of machine learning, statistics, computer vision, deep learning, natural language processing, and reinforcement learning. He has filed 14+ patents in India and the United States and has numerous research publications under his name. Hitesh has been associated in research roles at India's top B‐schools: Indian School of Business, Hyderabad, and the Indian Institute of Management, Ahmedabad. He is also actively involved in training and mentoring and has been invited as a guest speaker by various corporations and associations across the globe. He is an avid learner and enjoys reading books in his free time.
Kanchana Patlolla is an AI innovation program leader at Google Cloud. Previously she worked as an AI/ML specialist in Google Cloud Platform. She has architected solutions with major public cloud providers in financial services industries on their quest to the cloud, particularly in their Big Data and machine learning journey. In her spare time, she loves to try different cuisines and relax with her kids.
About the Technical Proofreader
Adam Vincent is an experienced educator with a passion for spreading knowledge and helping people expand their skill sets. He is multi‐certified in Google Cloud, is a Google Cloud Authorized Trainer, and has created multiple courses about machine learning. Adam also loves playing with data and automating everything. When he is not behind a screen, he enjoys playing tabletop games with friends and family, reading sci‐fi and fantasy novels, and hiking.
Google Technical Reviewer
Wiley and the authors wish to thank the Google Technical Reviewer Emma Freeman for her thorough review of the proofs for this book.
Why Become Professional ML Engineer (PMLE)
Certified?
There are several good reasons to get your PMLE certification.
Provides proof of professional achievement Certifications are quickly becoming status symbols in the computer service industry. Organizations, including members of the computer service industry, are recognizing the benefits of certification.
Increases your marketability According to Forbes (www.forbes.com/sites/louiscolumbus/2020/02/10/15-toppaying-it-certifications-in-2020/?sh=12f63aa8358e), jobs that require GCP certifications are the highest‐paying jobs for the second year in a row, paying an average salary of $175,761/year. So, there is a demand from many engineers to get certified. Of the many certifications that GCP offers, the AI/ML certified engineer is a new certification and is still evolving.
Provides an opportunity for advancement IDC's research (www.idc.com/getdoc.jsp?containerId=IDC_P40729) indicates that while AI/ML adoption is on the rise, the cost, lack of expertise, and lack of life cycle management tools are among the top three inhibitors to realizing AI and ML at scale.
This book is the first in the market to talk about Google Cloud AI/ML tools and the technology covering the latest Professional ML Engineer certification guidelines released on February 22, 2022.
Recognizes Google as a leader in open source and AI
Google is the main contributor to many of the path‐breaking open source softwares that dramatically changed the landscape of AI/ML, including TensorFlow, Kubeflow, Word2vec, BERT, and T5. Although these algorithms are in the open source domain, Google has the distinct ability of bringing these open source projects to the market through the Google Cloud Platform (GCP). In this regard, the other cloud providers are frequently seen as trailing Google's offering.
Raises customer confidence As the IT community, users, small business owners, and the like become more familiar with the PMLE certified professional, more of them will realize that the PMLE professional is more qualified to architect secure, cost‐effective, and scalable ML solutions on the Google Cloud environment than a noncertified individual.
How to Become Certified
You do not have to work for a particular company. It's not a secret society. There is no prerequisite to take this exam. However, there is a recommendation to have 3+ years of industry experience, including one or more years designing and managing solutions using Google Cloud.
This exam is 2 hours and has 50–60 multiple‐choice questions. You can register two ways for this exam:
Take the online‐proctored exam from anywhere or sitting at home. You can review the online testing requirements at www.webassessor.com/wa.do?
page=certInfo&branding=GOOGLECLOUD&tabs=13.
Take the on‐site, proctored exam at a testing center.
We usually prefer to go with the on‐site option as we like the focus time in a proctored environment. We have taken all our certifications in a test center. You can find and locate a test center near you at www.kryterion.com/Locate-Test-Center.
Who Should Buy This Book
This book is intended to help students, developers, data scientists, IT professionals, and ML engineers gain expertise in the ML technology on the Google Cloud Platform and take the Professional Machine Learning Engineer exam. This book intends to take readers through the machine learning process starting from data and moving on through feature engineering, model training, and deployment on the Google Cloud. It also walks readers through best practices for when
