Introduction

When customers have a business problem, say to detect objects in an image, sometimes it can be solved very well using machine learning. Google Cloud Platform (GCP) provides an extensive set of tools to be able to build a model that can accomplish this and deploy it for production usage. This book will cover many different use cases, such as using sales data to forecast for next quarter, identifying objects in images or videos, and even extracting information from text documents. This book helps an engineer build a secure, scalable, resilient machine learning application and automate the whole process using the latest technologies.

The purpose of this book is to help you pass the latest version of the Google Cloud Professional ML Engineer (PMLE) exam. Even after you've taken and passed the PMLE exam, this book should remain a useful reference as it covers the basics of machine learning, BigQuery ML, the Vertex AI platform, and MLOps.

Google Cloud Professional Machine Learning Engineer Certification

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML engineer considers responsible AI throughout the ML development process and collaborates closely with other job roles to ensure the long-term success of models. The ML engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML engineer designs and creates scalable solutions for optimal performance.

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-top-paying-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 to pick custom models versus AutoML or pretrained models. Google Cloud AI/ML technologies are presented through real-world scenarios to illustrate how IT professionals can design, build, and operate secure ML cloud environments to modernize and automate applications.

Anybody who wants to pass the Professional ML Engineer exam may benefit from this book. If you're new to Google Cloud, this book covers the updated machine learning exam course material, including the Google Cloud Vertex AI platform, MLOps, and BigQuery ML. This is the only book on the market to cover the complete Vertex AI platform, from bringing your data to training, tuning, and deploying your models.

Since it's a professional-level study guide, this book is written with the assumption that you know the basics of the Google Cloud Platform, such as compute, storage, networking, databases, and identity and access management (IAM) or have taken the Google Cloud Associate-level certification exam. Moreover, this book assumes you understand the basics of machine learning and data science in general. In case you do not understand a term or concept, we have included a glossary for your reference.

How This Book Is Organized

This book consists of 14 chapters plus supplementary information: a glossary, this introduction, and the assessment test after the introduction. The chapters are organized as follows:

• Chapter 1: Framing ML Problems This chapter covers how you can translate business challenges into ML use cases.
• Chapter 2: Exploring Data and Building Data Pipelines This chapter covers visualization, statistical fundamentals at scale, evaluation of data quality and feasibility, establishing data constraints (e.g., TFDV), organizing and optimizing training datasets, data validation, handling missing data, handling outliers, and data leakage.
• Chapter 3: Feature Engineering This chapter covers topics such as encoding structured data types, feature selection, class imbalance, feature crosses, and transformations (TensorFlow Transform).
• Chapter 4: Choosing the Right ML Infrastructure This chapter covers topics such as evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices) and choosing appropriate Google Cloud hardware components. It also covers choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements. It talks about how defining the model output should be used to solve the business problem. It also covers deciding how incorrect results should be handled and identifying data sources (available vs. ideal). It talks about AI solutions such as CCAI, DocAI, and Recommendations AI.
• Chapter 5: Architecting ML Solutions This chapter explains how to design reliable, scalable, and highly available ML solutions. Other topics include how you can choose appropriate ML services for a use case (e.g., Cloud Build, Kubeflow), component types (e.g., data collection, data management), automation, orchestration, and serving in machine learning.
• Chapter 6: Building Secure ML Pipelines This chapter describes how to build secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking). It also covers the privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI]).
• Chapter 7: Model Building This chapter describes the choice of framework and model parallelism. It also covers modeling techniques given interpretability requirements, transfer learning, data augmentation, semi-supervised learning, model generalization, and strategies to handle overfitting and...