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Google

Google Cloud Certified Professional Machine Learning Engineer

Google Cloud certification is just a course away. Train hard, test smarter, and transform data into ML solutions. 

(GCPMLE.AE1) / ISBN : 978-1-64459-591-6
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Über diesen Kurs

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!

So, get ready to get your hands dirty.

Fähigkeiten, die Sie erwerben werden

  • 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.

 

Kursübersicht herunterladen

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 Workbench Instance
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

Haben Sie Fragen? Schauen Sie sich die FAQs an

Read more on our basic statistics and data analysis course.

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Google Cloud Certified Professional ML Engineer is a top-tier credential that proves your skills in designing, building, and managing ML models on Google Cloud.

Anyone aiming to master ML on Google Cloud — data scientists, ML engineers, software developers, and even tech enthusiasts looking to improve their career.

A basic understanding of machine learning (ML) concepts, Python programming, and familiarity with Google Cloud tools will give you a head start, but we’ve got you covered on the essentials too.

The GCP ML Engineer certification includes multiple-choice and multiple-select questions, testing your practical knowledge in ML models, data pipelines, and Google Cloud tools.

The machine learning engineer certification costs $200 USD.

You’ll be ready for roles like Machine Learning Engineer, Data Scientist, Cloud Architect, and other AI-driven positions across top tech companies.

Prepare for Google Cloud ML Certification

  Think big & train smart to become the future of machine learning with Google Cloud! 

$ 279.99

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