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 Pre-built Components for Common Steps 93

When to Use Which Pipeline 93

Serving 94

Offline or Batch Prediction 94

Online Prediction 95

Summary 97

Exam Essentials 97

Review Questions 98

Chapter 6 Building Secure ML Pipelines 103

Building Secure ML Systems 104

Encryption at Rest 104

Encryption in Transit 105

Encryption in Use 105

Identity and Access Management 105

IAM Permissions for Vertex AI Workbench 106

Securing a Network with Vertex AI 109

Privacy Implications of Data Usage and Collection 113

Google Cloud Data Loss Prevention 114

Google Cloud Healthcare API for PHI Identification 115

Best Practices for Removing Sensitive Data 116

Summary 117

Exam Essentials 118

Review Questions 119

Chapter 7 Model Building 121

Choice of Framework and Model Parallelism 122

Data Parallelism 122

Model Parallelism 123

Modeling Techniques 125

Artificial Neural Network 126

Deep Neural Network (DNN) 126

Convolutional Neural Network 126

Recurrent Neural Network 127

What Loss Function to Use 127

Gradient Descent 128

Learning Rate 129

Batch 129

Batch Size 129

Epoch 129

Hyperparameters 129

Transfer Learning 130

Semi-supervised Learning 131

When You Need Semi-supervised Learning 131

Limitations of SSL 131

Data Augmentation 132

Offline Augmentation 132

Online Augmentation 132

Model Generalization and Strategies to Handle Overfitting and Underfitting 133

Bias Variance Trade- Off 133

Underfitting 133

Overfitting 134

Regularization 134

Summary 136

Exam Essentials 137

Review Questions 138

Chapter 8 Model Training and Hyperparameter Tuning 143

Ingestion of Various File Types into Training 145

Collect 146

Process 147

Store and Analyze 150

Developing Models in Vertex AI Workbench by Using Common Frameworks 151

Creating a Managed Notebook 153

Exploring Managed JupyterLab Features 154

Data Integration 155

BigQuery Integration 155

Ability to Scale the Compute Up or Down 156

Git Integration for Team Collaboration 156

Schedule or Execute a Notebook Code 158

Creating a User-Managed Notebook 159

Training a Model as a Job in Different Environments 161

Training Workflow with Vertex AI 162

Training Dataset Options in Vertex AI 163

Pre-built Containers 163

Custom Containers 166

Distributed Training 168

Hyperparameter Tuning 169

Why Hyperparameters Are Important 170

Techniques to Speed Up Hyperparameter Optimization 171

How Vertex AI Hyperparameter Tuning Works 171

Vertex AI Vizier 174

Tracking Metrics During Training 175

Interactive Shell 175

TensorFlow Profiler 177

What-If Tool 177

Retraining/Redeployment Evaluation 178

Data Drift 178

Concept Drift 178

When Should a Model Be Retrained? 178

Unit Testing for Model Training and Serving 179

Testing for Updates in API Calls 180

Testing for Algorithmic Correctness 180

Summary 180

Exam Essentials 181

Review Questions 182

Chapter 9 Model Explainability on Vertex AI 187

Model Explainability on Vertex AI 188

Explainable AI 188

Interpretability and Explainability 189

Feature Importance 189

Vertex Explainable AI 189

Data Bias and Fairness 193

ML Solution Readiness 194

How to Set Up Explanations in the Vertex AI 195

Summary 196

Exam Essentials 196

Review Questions 197

Chapter 10 Scaling Models in Production 199

Scaling Prediction Service 200

TensorFlow Serving 201

Serving (Online, Batch, and Caching) 203

Real- Time Static and Dynamic Reference Features 203

Pre-computing and Caching Prediction 206

Google Cloud Serving Options 207

Online Predictions 207

Batch Predictions 212

Hosting Third- Party Pipelines (MLFlow) on Google Cloud 213

Testing for Target Performance 214

Configuring Triggers and Pipeline Schedules 215

Summary 216

Exam Essentials 217

Review Questions 218

Chapter 11 Designing ML Training Pipelines 221

Orchestration Frameworks 223

Kubeflow Pipelines 224

Vertex AI Pipelines 225

Apache Airflow 228

Cloud Composer 229

Comparison of Tools 229

Identification of Components, Parameters, Triggers, and Compute Needs 230

Schedule the Workflows with Kubeflow Pipelines 230

Schedule Vertex AI Pipelines 232

System Design with Kubeflow/TFX 232

System Design with Kubeflow DSL 232

System Design with TFX 234

Hybrid or Multicloud Strategies 235

Summary 236

Exam Essentials 237

Review Questions 238

Chapter 12 Model Monitoring, Tracking, and Auditing Metadata 241

Model Monitoring 242

Concept Drift 242

Data Drift 243

Model Monitoring on Vertex AI 243

Drift and Skew Calculation 244

Input Schemas 245

Logging Strategy 247

Types of Prediction Logs 247

Log Settings 248

Model Monitoring and Logging 248

Model and Dataset Lineage 249

Vertex ML Metadata 249

Vertex AI Experiments 252

Vertex AI Debugging 253

Summary 253

Exam Essentials 254

Review Questions 255

Chapter 13 Maintaining ML Solutions 259

MLOps Maturity 260

MLOps Level 0: Manual/Tactical Phase 261

MLOps Level 1: Strategic Automation Phase 263

MLOps Level 2: CI/CD Automation, Transformational Phase 264

Retraining and Versioning Models 266

Triggers for Retraining 267

Versioning Models 267

Feature Store 268

Solution 268

Data Model 269

Ingestion and Serving 269

Vertex AI Permissions Model 270

Custom Service Account 270

Access Transparency in Vertex AI 271

Common Training and Serving Errors 271

Training Time Errors 271

Serving Time Errors 271

TensorFlow Data Validation 272

Vertex AI Debugging Shell 272

Summary 272

Exam Essentials 273

Review Questions 274

Chapter 14 BigQuery ML 279

BigQuery – Data Access 280

BigQuery ML Algorithms 282

Model Training 282

Model Evaluation 284

Prediction 285

Explainability in BigQuery ML 286

BigQuery ML vs. Vertex AI Tables 289

Interoperability with Vertex AI 289

Access BigQuery Public Dataset 289

Import BigQuery Data into Vertex AI 290

Access BigQuery Data from Vertex AI Workbench Notebooks 290

Analyze Test Prediction Data in BigQuery 290

Export Vertex AI Batch Prediction Results 290

Export BigQuery Models into Vertex AI 291

BigQuery Design Patterns 291

Hashed Feature 291

Transforms 291

Summary 292

Exam Essentials 293

Review Questions 294

Appendix Answers to Review Questions 299

Chapter 1: Framing ML Problems 300

Chapter 2: Exploring Data and Building Data Pipelines 301

Chapter 3: Feature Engineering 302

Chapter 4: Choosing the Right ML Infrastructure 302

Chapter 5: Architecting ML Solutions 304

Chapter 6: Building Secure ML Pipelines 305

Chapter 7: Model Building 306

Chapter 8: Model Training and Hyperparameter Tuning 307

Chapter 9: Model Explainability on Vertex AI 308

Chapter 10: Scaling Models in Production 308

Chapter 11: Designing ML Training Pipelines 309

Chapter 12: Model Monitoring, Tracking, and Auditing Metadata 310

Chapter 13: Maintaining ML Solutions 311

Chapter 14: BigQuery ML 313

Index 315