Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2024]
|
| Edición: | First edition |
| Colección: | SYBIDI document.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009786738506719 |
Tabla de Contenidos:
- Cover Page
- Title Page
- Copyright Page
- Acknowledgments
- About the Authors
- About the Technical Editors
- Contents at a Glance
- Contents
- Introduction
- Google Cloud Professional Machine Learning Engineer Certification
- Why Become Professional ML Engineer (PMLE) Certified?
- How to Become Certified
- Who Should Buy This Book
- How This Book Is Organized
- Chapter Features
- Bonus Digital Contents
- Conventions Used in This Book
- Google Cloud Professional ML Engineer Objective Map
- How to Contact the Publisher
- Chapter 1 Framing ML Problems
- Translating Business Use Cases
- Machine Learning Approaches
- Supervised, Unsupervised, and Semi-supervised Learning
- Classification, Regression, Forecasting, and Clustering
- ML Success Metrics
- Regression
- Responsible AI Practices
- Summary
- Exam Essentials
- Review Questions
- Chapter 2 Exploring Data and Building Data Pipelines
- Visualization
- Box Plot
- Line Plot
- Bar Plot
- Scatterplot
- Statistics Fundamentals
- Mean
- Median
- Mode
- Outlier Detection
- Standard Deviation
- Correlation
- Data Quality and Reliability
- Data Skew
- Data Cleaning
- Scaling
- Log Scaling
- Z-score
- Clipping
- Handling Outliers
- Establishing Data Constraints
- Exploration and Validation at Big-Data Scale
- Running TFDV on Google Cloud Platform
- Organizing and Optimizing Training Datasets
- Imbalanced Data
- Data Splitting
- Data Splitting Strategy for Online Systems
- Handling Missing Data
- Data Leakage
- Summary
- Exam Essentials
- Review Questions
- Chapter 3 Feature Engineering
- Consistent Data Preprocessing
- Encoding Structured Data Types
- Mapping Numeric Values
- Mapping Categorical Values
- Feature Selection
- Class Imbalance
- Classification Threshold with Precision and Recall
- Area under the Curve (AUC).
- Feature Crosses
- TensorFlow Transform
- TensorFlow Data API (tf.data)
- TensorFlow Transform
- GCP Data and ETL Tools
- Summary
- Exam Essentials
- Review Questions
- Chapter 4 Choosing the Right ML Infrastructure
- Pretrained vs. AutoML vs. Custom Models
- Pretrained Models
- Vision AI
- Video AI
- Natural Language AI
- Translation AI
- Speech-to-Text
- Text-to-Speech
- AutoML
- AutoML for Tables or Structured Data
- AutoML for Images and Video
- AutoML for Text
- Recommendations AI/Retail AI
- Document AI
- Dialogflow and Contact Center AI
- Custom Training
- How a CPU Works
- GPU
- TPU
- Provisioning for Predictions
- Scaling Behavior
- Finding the Ideal Machine Type
- Edge TPU
- Deploy to Android or iOS Device
- Summary
- Exam Essentials
- Review Questions
- Chapter 5 Architecting ML Solutions
- Designing Reliable, Scalable, and Highly Available ML Solutions
- Choosing an Appropriate ML Service
- Data Collection and Data Management
- Google Cloud Storage (GCS)
- BigQuery
- Vertex AI Managed Datasets
- Vertex AI Feature Store
- NoSQL Data Store
- Automation and Orchestration
- Use Vertex AI Pipelines to Orchestrate the ML Workflow
- Use Kubeflow Pipelines for Flexible Pipeline Construction
- Use TensorFlow Extended SDK to Leverage Pre-built Components for Common Steps
- When to Use Which Pipeline
- Serving
- Offline or Batch Prediction
- Online Prediction
- Summary
- Exam Essentials
- Review Questions
- Chapter 6 Building Secure ML Pipelines
- Building Secure ML Systems
- Encryption at Rest
- Encryption in Transit
- Encryption in Use
- Identity and Access Management
- IAM Permissions for Vertex AI Workbench
- Securing a Network with Vertex AI
- Privacy Implications of Data Usage and Collection
- Google Cloud Data Loss Prevention
- Google Cloud Healthcare API for PHI Identification.
- Best Practices for Removing Sensitive Data
- Summary
- Exam Essentials
- Review Questions
- Chapter 7 Model Building
- Choice of Framework and Model Parallelism
- Data Parallelism
- Model Parallelism
- Modeling Techniques
- Artificial Neural Network
- Deep Neural Network (DNN)
- Convolutional Neural Network
- Recurrent Neural Network
- What Loss Function to Use
- Gradient Descent
- Learning Rate
- Batch
- Batch Size
- Epoch
- Hyperparameters
- Transfer Learning
- Semi-supervised Learning
- When You Need Semi-supervised Learning
- Limitations of SSL
- Data Augmentation
- Offline Augmentation
- Online Augmentation
- Model Generalization and Strategies to Handle Overfitting and Underfitting
- Bias Variance Trade-Off
- Underfitting
- Overfitting
- Regularization
- Summary
- Exam Essentials
- Review Questions
- Chapter 8 Model Training and Hyperparameter Tuning
- Ingestion of Various File Types into Training
- Collect
- Process
- Store and Analyze
- Developing Models in Vertex AI Workbench by Using Common Frameworks
- Creating a Managed Notebook
- Exploring Managed JupyterLab Features
- Data Integration
- BigQuery Integration
- Ability to Scale the Compute Up or Down
- Git Integration for Team Collaboration
- Schedule or Execute a Notebook Code
- Creating a User-Managed Notebook
- Training a Model as a Job in Different Environments
- Training Workflow with Vertex AI
- Training Dataset Options in Vertex AI
- Pre-built Containers
- Custom Containers
- Distributed Training
- Hyperparameter Tuning
- Why Hyperparameters Are Important
- Techniques to Speed Up Hyperparameter Optimization
- How Vertex AI Hyperparameter Tuning Works
- Vertex AI Vizier
- Tracking Metrics During Training
- Interactive Shell
- TensorFlow Profiler
- What-If Tool
- Retraining/Redeployment Evaluation
- Data Drift.
- Concept Drift
- When Should a Model Be Retrained?
- Unit Testing for Model Training and Serving
- Testing for Updates in API Calls
- Testing for Algorithmic Correctness
- Summary
- Exam Essentials
- Review Questions
- Chapter 9 Model Explainability on Vertex AI
- Model Explainability on Vertex AI
- Explainable AI
- Interpretability and Explainability
- Feature Importance
- Vertex Explainable AI
- Data Bias and Fairness
- ML Solution Readiness
- How to Set Up Explanations in the Vertex AI
- Summary
- Exam Essentials
- Review Questions
- Chapter 10 Scaling Models in Production
- Scaling Prediction Service
- TensorFlow Serving
- Serving (Online, Batch, and Caching)
- Real-Time Static and Dynamic Reference Features
- Pre-computing and Caching Prediction
- Google Cloud Serving Options
- Online Predictions
- Batch Predictions
- Hosting Third-Party Pipelines (MLflow) on Google Cloud
- Testing for Target Performance
- Configuring Triggers and Pipeline Schedules
- Summary
- Exam Essentials
- Review Questions
- Chapter 11 Designing ML Training Pipelines
- Orchestration Frameworks
- Kubeflow Pipelines
- Vertex AI Pipelines
- Apache Airflow
- Cloud Composer
- Comparison of Tools
- Identification of Components, Parameters, Triggers, and Compute Needs
- Schedule the Workflows with Kubeflow Pipelines
- Schedule Vertex AI Pipelines
- System Design with Kubeflow/TFX
- System Design with Kubeflow DSL
- System Design with TFX
- Hybrid or Multicloud Strategies
- Summary
- Exam Essentials
- Review Questions
- Chapter 12 Model Monitoring, Tracking, and Auditing Metadata
- Model Monitoring
- Concept Drift
- Data Drift
- Model Monitoring on Vertex AI
- Drift and Skew Calculation
- Input Schemas
- Logging Strategy
- Types of Prediction Logs
- Log Settings
- Model Monitoring and Logging
- Model and Dataset Lineage.
- Vertex ML Metadata
- Vertex AI Experiments
- Vertex AI Debugging
- Summary
- Exam Essentials
- Review Questions
- Chapter 13 Maintaining ML Solutions
- MLOps Maturity
- MLOps Level 0: Manual/Tactical Phase
- MLOps Level 1: Strategic Automation Phase
- MLOps Level 2: CI/CD Automation, Transformational Phase
- Retraining and Versioning Models
- Triggers for Retraining
- Versioning Models
- Feature Store
- Solution
- Data Model
- Ingestion and Serving
- Vertex AI Permissions Model
- Custom Service Account
- Access Transparency in Vertex AI
- Common Training and Serving Errors
- Training Time Errors
- Serving Time Errors
- TensorFlow Data Validation
- Vertex AI Debugging Shell
- Summary
- Exam Essentials
- Review Questions
- Chapter 14 BigQuery ML
- BigQuery - Data Access
- BigQuery ML Algorithms
- Model Training
- Model Evaluation
- Prediction
- Explainability in BigQuery ML
- BigQuery ML vs. Vertex AI Tables
- Interoperability with Vertex AI
- Access BigQuery Public Dataset
- Import BigQuery Data into Vertex AI
- Access BigQuery Data from Vertex AI Workbench Notebooks
- Analyze Test Prediction Data in BigQuery
- Export Vertex AI Batch Prediction Results
- Export BigQuery Models into Vertex AI
- BigQuery Design Patterns
- Hashed Feature
- Transforms
- Summary
- Exam Essentials
- Review Questions
- Appendix: Answers to Review Questions
- Chapter 1: Framing ML Problems
- Chapter 2: Exploring Data and Building Data Pipelines
- Chapter 3: Feature Engineering
- Chapter 4: Choosing the Right ML Infrastructure
- Chapter 5: Architecting ML Solutions
- Chapter 6: Building Secure ML Pipelines
- Chapter 7: Model Building
- Chapter 8: Model Training and Hyperparameter Tuning
- Chapter 9: Model Explainability on Vertex AI
- Chapter 10: Scaling Models in Production.
- Chapter 11: Designing ML Training Pipelines.
