Official Google Cloud Certified Professional Machine Learning - Engineer Study Guid

Expert, guidance for the Google Cloud Machine Learning certification exam
 
In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you'll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer.
 
The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments.
 
The book also shows you how to:
* Frame ML problems and architect ML solutions from scratch
* Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools
* Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards
 
A can't-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.

List of contents

Introduction xxi
 
Assessment Testxxxii
 
Chapter 1 Framing ML Problems 1
 
Translating Business Use Cases 3
 
Machine Learning Approaches 5
 
Supervised, Unsupervised, and Semi- supervised Learning 5
 
Classification, Regression, Forecasting, and Clustering 7
 
ML Success Metrics 8
 
Regression 12
 
Responsible AI Practices 13
 
Summary 14
 
Exam Essentials 14
 
Review Questions 15
 
Chapter 2 Exploring Data and Building Data Pipelines 19
 
Visualization 20
 
Box Plot 20
 
Line Plot 21
 
Bar Plot 21
 
Scatterplot 22
 
Statistics Fundamentals 22
 
Mean 22
 
Median 22
 
Mode 23
 
Outlier Detection 23
 
Standard Deviation 23
 
Correlation 24
 
Data Quality and Reliability 24
 
Data Skew 25
 
Data Cleaning 25
 
Scaling 25
 
Log Scaling 26
 
Z-score 26
 
Clipping 26
 
Handling Outliers 26
 
Establishing Data Constraints 27
 
Exploration and Validation at Big- Data Scale 27
 
Running TFDV on Google Cloud Platform 28
 
Organizing and Optimizing Training Datasets 29
 
Imbalanced Data 29
 
Data Splitting 31
 
Data Splitting Strategy for Online Systems 31
 
Handling Missing Data 32
 
Data Leakage 33
 
Summary 34
 
Exam Essentials 34
 
Review Questions 36
 
Chapter 3 Feature Engineering 39
 
Consistent Data Preprocessing 40
 
Encoding Structured Data Types 41
 
Mapping Numeric Values 42
 
Mapping Categorical Values 42
 
Feature Selection 44
 
Class Imbalance 44
 
Classification Threshold with Precision and Recall 45
 
Area under the Curve (AUC) 46
 
Feature Crosses 46
 
TensorFlow Transform 49
 
TensorFlow Data API (tf.data) 49
 
TensorFlow Transform 49
 
GCP Data and ETL Tools 51
 
Summary 51
 
Exam Essentials 52
 
Review Questions 53
 
Chapter 4 Choosing the Right ML Infrastructure 57
 
Pretrained vs. AutoML vs. Custom Models 58
 
Pretrained Models 60
 
Vision AI 61
 
Video AI 62
 
Natural Language AI 62
 
Translation AI 63
 
Speech- to- Text 63
 
Text- to- Speech 64
 
AutoML 64
 
AutoML for Tables or Structured Data 64
 
AutoML for Images and Video 66
 
AutoML for Text 67
 
Recommendations AI/Retail AI 68
 
Document AI 69
 
Dialogflow and Contact Center AI 69
 
Custom Training 70
 
How a CPU Works 71
 
GPU 71
 
TPU 72
 
Provisioning for Predictions 74
 
Scaling Behavior 75
 
Finding the Ideal Machine Type 75
 
Edge TPU 76
 
Deploy to Android or iOS Device 76
 
Summary 77
 
Exam Essentials 77
 
Review Questions 78
 
Chapter 5 Architecting ML Solutions 83
 
Designing Reliable, Scalable, and Highly Available ml Solutions 84
 
Choosing an Appropriate ML Service 86
 
Data Collection and Data Management 87
 
Google Cloud Storage (GCS) 88
 
BigQuery 88
 
Vertex AI Managed Datasets 89
 
Vertex AI Feature Store 89
 
NoSQL Data Store 90
 
Automation and Orchestration 91
 
Use Vertex AI Pipelines to Orchestrate the ML Workflow 92
 
Use Kubeflow Pipelines for Flexible Pipeline Construction 92
 
Use TensorFlow Extended SDK to Leverage

About the author

ABOUT THE AUTHORS MONA is an AI/ML specialist in the Google Public Sector. She is the author of Natural Language Processing with AWS AI Services and a frequent speaker at cloud computing and machine learning events. She was a Sr. AI/ML specialist SA at AWS before joining Google. She has 14 Certifications and has created courses for AWS AI/ML Certification Speciality Exam readiness. She has authored 17 articles on AI/ML and also co-authored a research paper on CORD-19 Neural Search, which won an award at the AAAI Conference on Artificial Intelligence Pratap Ramamurthy is an AI/ML Specialist Customer Engineer in Google Cloud. Previously, he worked as a Sr. Principal Solution Architect at H2O.ai and before that was a Partner Solution Architect at AWS. He has authored several research papers and holds 3 patents.

Summary

Expert, guidance for the Google Cloud Machine Learning certification exam

In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you'll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer.

The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments.

The book also shows you how to:
* Frame ML problems and architect ML solutions from scratch
* Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools
* Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards

A can't-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.

Product details

• Authors: Mona Mona, Mona Ramamurthy Mona, Pratap Ramamurthy
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 14.12.2023
• EAN: 9781119944461
• ISBN: 978-1-119-94446-1
• No. of pages: 368
• Series: Sybex Study Guide
• Subjects: Guides > Self-help, everyday life > Family; Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Informatik, Prüfungsvorbereitung, Zertifizierung, computer science, google cloud, test prep, Zertifizierung f. MSCE u. Novell, Certification (MSCE, Novell, etc.), Grid & Cloud Computing, Grid- u. Cloud-Computing