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Google Cloud Certified Professional Machine Learning Engineer

(GCPMLE.AE1) / ISBN : 978-1-64459-591-6
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About This Course

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. 

Skills You’ll Get

The Google Cloud Professional Machine Learning Engineer (PMLE) certification is designed to validate the expertise of professionals in designing, building, and deploying machine learning models on Google Cloud. This certification demonstrates a candidate's ability to leverage Google Cloud technologies to solve business problems using machine learning.

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
  • Building an ANN Model
7

Maintaining ML Solutions

  • Using TensorFlow Data Validation (TFDV)
8

BigQuery ML

  • Creating a Model in BigQuery

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