Google Cloud Certified Professional Machine Learning Engineer

Hours: 40 / Access Length: 12 Months / Delivery: Online, Self-Paced
Retail Price: $734.00

Course Overview:

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!

Students will:
  • 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 Outline:

Lesson 1: Introduction
Lesson 2: Framing ML Problems
  • Translating Business Use Cases
  • Machine Learning Approaches
  • ML Success Metrics
  • Responsible AI Practices
  • Summary
  • Exam Essentials
Lesson 3: 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
Lesson 4: Feature Engineering
  • Consistent Data Preprocessing
  • Encoding Structured Data Types
  • Class Imbalance
  • Feature Crosses
  • TensorFlow Transform
  • GCP Data and ETL Tools
  • Summary
  • Exam Essentials
Lesson 5: Choosing the Right ML Infrastructure
  • Pretrained vs. AutoML vs. Custom Models
  • Pretrained Models
  • AutoML
  • Custom Training
  • Provisioning for Predictions
  • Summary
  • Exam Essentials
Lesson 6: 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
Lesson 7: Building Secure ML Pipelines
  • Building Secure ML Systems
  • Identity and Access Management
  • Privacy Implications of Data Usage and Collection
  • Summary
  • Exam Essentials
Lesson 8: 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
Lesson 9: 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
Lesson 10: Model Explainability on Vertex AI
  • Model Explainability on Vertex AI
  • Summary
  • Exam Essentials
Lesson 11: 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
Lesson 12: 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
Lesson 13: 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
Lesson 14: Maintaining ML Solutions
  • MLOps Maturity
  • Retraining and Versioning Models
  • Feature Store
  • Vertex AI Permissions Model
  • Common Training and Serving Errors
  • Summary
  • Exam Essentials
Lesson 15: 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

All necessary course materials are included.

Certification(s):

This course prepares a student to take the Google Cloud Certified Professional Machine Learning Engineer national certification exam.


System Requirements:

Internet Connectivity Requirements:

  • Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

  • CPU: 1 GHz or higher
  • RAM: 4 GB or higher
  • Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
  • Speakers / Headphones
  • Microphone for Webinar or Live Online sessions.

Operating System Requirements:

  • Windows 7 or higher.
  • Mac OSX 10 or higher.
  • Latest Chrome OS
  • Latest Linux Distributions

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

  • Latest Google Chrome is recommended for the best experience.
  • Latest Mozilla FireFox
  • Latest Microsoft Edge
  • Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

  • Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
  • PDF reader program (Adobe Reader, FoxIt)
  • Courses may require other software that is described in the above course outline.


** The course outlines displayed on this website are subject to change at any time without prior notice. **