Google Cloud Certified Professional Machine Learning Engineer
(GCPMLE.AE1) / ISBN : 978-1-64459-591-6
About This Course
The Google Cloud Professional Machine Learning Engineer course equips you with the skills to design, build, and deploy sophisticated machine learning models on Google Cloud. You'll dive deep into key topics like framing ML problems, architecting scalable ML solutions, developing and optimizing models, automating end-to-end ML pipelines, and monitoring model performance. This course is ideal for experienced Google Cloud users who want to take their machine-learning skills to the next level.
Skills You’ll Get
The Google Cloud Professional Machine Learning Engineer (PMLE) certification is designed to validate the expertise of professionals in designing, building, and deploying machine learning models on Google Cloud. This certification demonstrates a candidate's ability to leverage Google Cloud technologies to solve business problems using machine learning.
Get the support you need. Enroll in our Instructor-Led Course.
Interactive Lessons
15+ Interactive Lessons | 341+ Exercises | 194+ Quizzes | 70+ Flashcards | 70+ Glossary of terms
Gamified TestPrep
60+ Pre Assessment Questions | 2+ Full Length Tests | 65+ Post Assessment Questions | 120+ Practice Test Questions
Hands-On Labs
12+ LiveLab | 12+ Video tutorials | 28+ Minutes
Introduction
- Google Cloud Professional Machine Learning Engineer Certification
- Who Should Buy This Course
- How This Course Is Organized
- Conventions Used in This Course
- Google Cloud Professional ML Engineer Objective Map
Framing ML Problems
- Translating Business Use Cases
- Machine Learning Approaches
- ML Success Metrics
- Responsible AI Practices
- Summary
- Exam Essentials
Exploring Data and Building Data Pipelines
- Visualization
- Statistics Fundamentals
- Data Quality and Reliability
- Establishing Data Constraints
- Running TFDV on Google Cloud Platform
- Organizing and Optimizing Training Datasets
- Handling Missing Data
- Data Leakage
- Summary
- Exam Essentials
Feature Engineering
- Consistent Data Preprocessing
- Encoding Structured Data Types
- Class Imbalance
- Feature Crosses
- TensorFlow Transform
- GCP Data and ETL Tools
- Summary
- Exam Essentials
Choosing the Right ML Infrastructure
- Pretrained vs. AutoML vs. Custom Models
- Pretrained Models
- AutoML
- Custom Training
- Provisioning for Predictions
- Summary
- Exam Essentials
Architecting ML Solutions
- Designing Reliable, Scalable, and Highly Available ML Solutions
- Choosing an Appropriate ML Service
- Data Collection and Data Management
- Automation and Orchestration
- Serving
- Summary
- Exam Essentials
Building Secure ML Pipelines
- Building Secure ML Systems
- Identity and Access Management
- Privacy Implications of Data Usage and Collection
- Summary
- Exam Essentials
Model Building
- Choice of Framework and Model Parallelism
- Modeling Techniques
- Transfer Learning
- Semi‐supervised Learning
- Data Augmentation
- Model Generalization and Strategies to Handle Overfitting and Underfitting
- Summary
- Exam Essentials
Model Training and Hyperparameter Tuning
- Ingestion of Various File Types into Training
- Developing Models in Vertex AI Workbench by Using Common Frameworks
- Training a Model as a Job in Different Environments
- Hyperparameter Tuning
- Tracking Metrics During Training
- Retraining/Redeployment Evaluation
- Unit Testing for Model Training and Serving
- Summary
- Exam Essentials
Model Explainability on Vertex AI
- Model Explainability on Vertex AI
- Summary
- Exam Essentials
Scaling Models in Production
- Scaling Prediction Service
- Serving (Online, Batch, and Caching)
- Google Cloud Serving Options
- Hosting Third‐Party Pipelines (MLflow) on Google Cloud
- Testing for Target Performance
- Configuring Triggers and Pipeline Schedules
- Summary
- Exam Essentials
Designing ML Training Pipelines
- Orchestration Frameworks
- Identification of Components, Parameters, Triggers, and Compute Needs
- System Design with Kubeflow/TFX
- Hybrid or Multicloud Strategies
- Summary
- Exam Essentials
Model Monitoring, Tracking, and Auditing Metadata
- Model Monitoring
- Model Monitoring on Vertex AI
- Logging Strategy
- Model and Dataset Lineage
- Vertex AI Experiments
- Vertex AI Debugging
- Summary
- Exam Essentials
Maintaining ML Solutions
- MLOps Maturity
- Retraining and Versioning Models
- Feature Store
- Vertex AI Permissions Model
- Common Training and Serving Errors
- Summary
- Exam Essentials
BigQuery ML
- BigQuery – Data Access
- BigQuery ML Algorithms
- Explainability in BigQuery ML
- BigQuery ML vs. Vertex AI Tables
- Interoperability with Vertex AI
- BigQuery Design Patterns
- Summary
- Exam Essentials
Exploring Data and Building Data Pipelines
- Splitting Data
- Transforming Categorical Data into Numerical Data
Feature Engineering
- Performing EDA
- Using Tensorflow Transform
Choosing the Right ML Infrastructure
- Using Natural Language AI
Architecting ML Solutions
- Storing Data in BigQuery
Building Secure ML Pipelines
- Creating a User-Managed Notebook
Model Building
- Building a DNN Network
- Building an ANN Model
Maintaining ML Solutions
- Using TensorFlow Data Validation (TFDV)
BigQuery ML
- Creating a Model in BigQuery
- Importing BigQuery Data into Vertex AI
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Contact Us NowUSD 200 (plus taxes where applicable)
Multiple-choice and Multiple-select questions
The exam contains 50-60 questions.
120 minutes
Here are the retake policies:
- Cloud Digital Leader: you have a maximum of ten attempts within a one year period and must wait at least 14 days between each failed attempt.
- Associate and Professional certification exams: you have a maximum of four attempts in two years. If you don't pass the exam, you can take it again after 14 days. If you don't pass the second time, you must wait 60 days before taking it a third time. If you don't pass the third time, you must wait 365 days before taking it a fourth time.