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.