Google Cloud Certified Professional Machine Learning Engineer

What you will learn

• 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

1. 1

   Lesson 1: Introduction

2. 2

   Lesson 2: Framing ML Problems

   • Translating Business Use Cases
   • Machine Learning Approaches
   • ML Success Metrics
   • Responsible AI Practices
   • Summary
   • Exam Essentials
3. 3

   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
4. 4

   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
5. 5

   Lesson 5: Choosing the Right ML Infrastructure

   • Pretrained vs. AutoML vs. Custom Models
   • Pretrained Models
   • AutoML
   • Custom Training
   • Provisioning for Predictions
   • Summary
   • Exam Essentials
6. 6

   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
7. 7

   Lesson 7: Building Secure ML Pipelines

   • Building Secure ML Systems
   • Identity and Access Management
   • Privacy Implications of Data Usage and Collection
   • Summary
   • Exam Essentials
8. 8

   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
9. 9

   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
10. 10

    Lesson 10: Model Explainability on Vertex AI

    • Model Explainability on Vertex AI
    • Summary
    • Exam Essentials
11. 11

    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
12. 12

    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
13. 13

    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
14. 14

    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
15. 15

    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

Certification

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