Mona Mona, Pratap Ramamurthy

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 352
• Erscheinungstermin: 27. Oktober 2023
• Englisch
• ISBN-13: 9781119981565
• Artikelnr.: 69297079

Autorenporträt

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.

Inhaltsangabe

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