GCPMLE.AE1
Google Cloud Certified Professional Machine Learning Engineer
Google Cloud certification is just a course away. Train hard, test smarter, and transform data into ML solutions.
- Practice in 11 Hands-On Labs — nothing to install
- 15 Interactive Lessons and 105 topics mapped to the official exam objectives
- 475 Practice Test Questions and 2 Full Length Tests
Expert Self-paced · 1 year access
11 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
15Interactive Lessons
105Topics
11LiveLab
475Practice Test Questions
70Flashcards
70Glossary of terms
01 / Skills you'll get
What you will be able to do
Try Free →
No credit card required
This Google Cloud ML engineer course takes you on a fast track through all the core concepts and practical skills you need, from building data pipelines to scaling models in production.
With hands-on labs, you’ll learn how to architect secure, reliable, and scalable ML solutions that get results — fast!
So, get ready to get your hands dirty.
- Personalize your Google Workspace with custom actions and folders.
- Build scalable machine learning (ML) pipelines using Google Cloud tools like Vertex AI and Big Query.
- Optimize data pipelines and handle challenges like missing data and data leakage with real-world techniques.
- Design secure and reliable ML solutions that meet business needs while adhering to responsible AI practices.
- Master feature engineering, data preprocessing, and encoding for improved model performance.
- Leverage pretrained models, AutoML, and custom models to choose the best infrastructure for your ML projects.
- Train and tune models, utilizing advanced strategies like hyperparameter optimization and transfer learning.
- Monitor and track model performance using Vertex AI, ensuring continuous improvement and scalability.
- Implement MLOps best practices for model retraining, versioning, and error handling in production environments.
- Use BigQuery ML to streamline data analysis and model building without complex coding.
- Ensure data privacy and security by building and managing secure ML pipelines with Google Cloud’s IAM tools.
Course Highlights
-
15 Structured Lessons Comprehensive coverage of core course objectives
-
11 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
-
475 Practice Questions Assessment tests with detailed answer rationales
-
1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
15 Interactive Lessons · 105 topics01 Introduction 5 topics +
- 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
02 Framing ML Problems 6 topics +
- Translating Business Use Cases
- Machine Learning Approaches
- ML Success Metrics
- Responsible AI Practices
- Summary
- Exam Essentials
03 Exploring Data and Building Data Pipelines 10 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
04 Feature Engineering 8 topics · 2 LiveLab +
- Consistent Data Preprocessing
- Encoding Structured Data Types
- Class Imbalance
- Feature Crosses
- TensorFlow Transform
- GCP Data and ETL Tools
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
05 Choosing the Right ML Infrastructure 7 topics · 1 LiveLab +
- Pretrained vs. AutoML vs. Custom Models
- Pretrained Models
- AutoML
- Custom Training
- Provisioning for Predictions
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
06 Architecting ML Solutions 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
07 Building Secure ML Pipelines 5 topics · 1 LiveLab +
- Building Secure ML Systems
- Identity and Access Management
- Privacy Implications of Data Usage and Collection
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
08 Model Building 8 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
09 Model Training and Hyperparameter Tuning 9 topics +
- 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
10 Model Explainability on Vertex AI 3 topics +
- Model Explainability on Vertex AI
- Summary
- Exam Essentials
11 Scaling Models in Production 8 topics +
- 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
12 Designing ML Training Pipelines 6 topics +
- Orchestration Frameworks
- Identification of Components, Parameters, Triggers, and Compute Needs
- System Design with Kubeflow/TFX
- Hybrid or Multicloud Strategies
- Summary
- Exam Essentials
13 Model Monitoring, Tracking, and Auditing Metadata 8 topics +
- Model Monitoring
- Model Monitoring on Vertex AI
- Logging Strategy
- Model and Dataset Lineage
- Vertex AI Experiments
- Vertex AI Debugging
- Summary
- Exam Essentials
14 Maintaining ML Solutions 7 topics · 1 LiveLab +
- MLOps Maturity
- Retraining and Versioning Models
- Feature Store
- Vertex AI Permissions Model
- Common Training and Serving Errors
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
15 BigQuery ML 8 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
11 LiveLabs- Splitting Data
- Transforming Categorical Data into Numerical Data
- Performing EDA
- Using Tensorflow Transform
- Using Natural Language AI
- Storing Data in BigQuery
- Creating a Workbench Instance
- Building a DNN
- Building an ANN Model
- Using TensorFlow Data Validation (TFDV)
- Creating a Model in BigQuery
Labs run in your browser — nothing to install.
03 / Exam details
Google Cloud Certified Professional Machine Learning Engineer Details
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.
Questions
50-60
(per exam)
Duration
120 minutes
(per exam)
Exam Fee
USD 200 (plus taxes where applicable)
(per exam)
Delivered by
Google
(in-person or online)
Question Format
Multiple-choice and Multiple-select questions
single/multiple choice, performance-based
Practice Tests
475 Practice Questions
(mapped to official exam objectives)
Certification
GCPMLE.AE1
Google credential
Ready to take the exam?
Add your official GCPMLE.AE1 exam voucher to your order.
Official Voucher · Fast delivery · Retake bundle available
Exam voucher is sold separately and not included with standard course.
04 / FAQs
Questions before you start
What is the Google Cloud Certified Professional Machine Learning Engineer certification?+
Google Cloud Certified Professional ML Engineer is a top-tier credential that proves your skills in designing, building, and managing ML models on Google Cloud.
Who should take this certification online course?+
Anyone aiming to master ML on Google Cloud — data scientists, ML engineers, software developers, and even tech enthusiasts looking to improve their career.
What are the prerequisites for the course?+
A basic understanding of machine learning (ML) concepts, Python programming, and familiarity with Google Cloud tools will give you a head start, but we’ve got you covered on the essentials too.
What is the format of the Google Cloud ML Engineer certification exam?+
The GCP ML Engineer certification includes multiple-choice and multiple-select questions, testing your practical knowledge in ML models, data pipelines, and Google Cloud tools.
How much does the certification exam cost?+
The machine learning engineer certification costs $200 USD.
What job roles can I pursue after completing this online course?+
You’ll be ready for roles like Machine Learning Engineer, Data Scientist, Cloud Architect, and other AI-driven positions across top tech companies.
What is the exam registration fee?+
USD 200 (plus taxes where applicable)
Where do I take the exam?+
Google
What is the format of the exam?+
Multiple-choice and Multiple-select questions
How many questions are asked in the exam?+
The exam contains 50-60 questions.
What is the duration of the exam?+
120 minutes
What is the exam's retake policy?+
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.
Where can I find more information about this exam?+
Keep going
You might also like
Prepare for Google Cloud ML Certification
Think big & train smart to become the future of machine learning with Google Cloud!
- 1 year of full access
- 11 LiveLab included
- Certificate of completion
No credit card required