Google

Google Cloud Certified Professional Machine Learning Engineer

(GCPMLE.AE1) / ISBN : 978-1-64459-591-6
This course includes
Interactive Lessons
Gamified TestPrep
Hands-On Labs
AI Tutor (Add-on)
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About This Course

Skills You’ll Get

Download Course Outline

1

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
2

Framing ML Problems

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

Feature Engineering

  • Consistent Data Preprocessing
  • Encoding Structured Data Types
  • Class Imbalance
  • Feature Crosses
  • TensorFlow Transform
  • GCP Data and ETL Tools
  • Summary
  • Exam Essentials
5

Choosing the Right ML Infrastructure

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

Building Secure ML Pipelines

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

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

Model Explainability on Vertex AI

  • Model Explainability on Vertex AI
  • Summary
  • Exam Essentials
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

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

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

Maintaining ML Solutions

  • MLOps Maturity
  • Retraining and Versioning Models
  • Feature Store
  • Vertex AI Permissions Model
  • Common Training and Serving Errors
  • Summary
  • Exam Essentials
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

1

Exploring Data and Building Data Pipelines

  • Splitting Data
  • Transforming Categorical Data into Numerical Data
2

Feature Engineering

  • Performing EDA
  • Using Tensorflow Transform
3

Choosing the Right ML Infrastructure

  • Using Natural Language AI
4

Architecting ML Solutions

  • Storing Data in BigQuery
5

Building Secure ML Pipelines

  • Creating a User-Managed Notebook
6

Model Building

  • Building a DNN Network
  • Building an ANN Model
7

Maintaining ML Solutions

  • Using TensorFlow Data Validation (TFDV)
8

BigQuery ML

  • Creating a Model in BigQuery
  • Importing BigQuery Data into Vertex AI

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