GCPMLE.AE1
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.
- Practice in 11 Praktische Übungen — nothing to install
- 15 Interaktive Lektionen Und 105 topics mapped to the official exam objectives
- 475 Testfragen zur Praxis Und 2 Ausführliche Tests
Expert Selbstgesteuert · 1 Jahr Zugang
11 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
15Interaktive Lektionen
105Topics
11LiveLab
475Testfragen zur Praxis
70Karteikarten
70Glossar der Begriffe
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Try Free →
Keine Kreditkarte benötigt
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.
- 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 Highlights
-
15 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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11 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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475 Übungsfragen Bewertungstests mit ausführlichen Antwortbegründungen
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
15 Interaktive Lektionen · 105 topics01 Introduction 5 topics +
- 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
02 Framing ML Problems 6 topics +
- Translating Business Use Cases
- Machine Learning Approaches
- ML Success Metrics
- Responsible AI Practices
- Summary
- Exam Essentials
03 Exploring Data and Building Data Pipelines 10 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
04 Feature Engineering 8 topics · 2 LiveLab +
- Consistent Data Preprocessing
- Encoding Structured Data Types
- Class Imbalance
- Feature Crosses
- TensorFlow Transform
- GCP Data and ETL Tools
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
05 Choosing the Right ML Infrastructure 7 topics · 1 LiveLab +
- Pretrained vs. AutoML vs. Custom Models
- Pretrained Models
- AutoML
- Custom Training
- Provisioning for Predictions
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
06 Architecting ML Solutions 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
07 Building Secure ML Pipelines 5 topics · 1 LiveLab +
- Building Secure ML Systems
- Identity and Access Management
- Privacy Implications of Data Usage and Collection
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
08 Model Building 8 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
09 Model Training and Hyperparameter Tuning 9 topics +
- 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 3 topics +
- Model Explainability on Vertex AI
- Summary
- Exam Essentials
11 Scaling Models in Production 8 topics +
- 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 6 topics +
- 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 8 topics +
- 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 7 topics · 1 LiveLab +
- MLOps Maturity
- Retraining and Versioning Models
- Feature Store
- Vertex AI Permissions Model
- Common Training and Serving Errors
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
15 BigQuery ML 8 topics · 1 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
Praktische Übungen Our edge
11 LiveLabs- Splitting Data
- Transforming Categorical Data into Numerical Data
- Performing EDA
- Using Tensorflow Transform
- Using Natural Language AI
- Storing Data in BigQuery
- Creating a Workbench Instance
- Building a DNN
- Building an ANN Model
- Using TensorFlow Data Validation (TFDV)
- Creating a Model in BigQuery
Labore laufen direkt im Browser — nichts zu installieren.
03 / Prüfungsdetails
Google Cloud Certified Professional Machine Learning Engineer Details
Der Kurs „Google Cloud Professional Machine Learning Engineer“ vermittelt Ihnen die Fähigkeiten, anspruchsvolle Machine-Learning-Modelle auf Google Cloud zu entwerfen, zu erstellen und bereitzustellen. Sie tauchen tief in wichtige Themen ein, wie z. B. die Formulierung von ML-Problemen, die Architektur skalierbarer ML-Lösungen, die Entwicklung und Optimierung von Modellen, die Automatisierung von End-to-End-ML-Pipelines und die Überwachung der Modellleistung. Dieser Kurs ist ideal für erfahrene Google Cloud-Nutzer, die ihre Machine-Learning-Kenntnisse auf die nächste Stufe heben möchten.
Questions
50-60
(pro Prüfung)
Duration
120 Minuten
(pro Prüfung)
Exam Fee
USD 200 (plus taxes where applicable)
(pro Prüfung)
Delivered by
Google
(vor Ort oder online)
Question Format
Multiple-choice and Multiple-select questions
Einzel-/Mehrfachauswahl, leistungsbasiert
Übungstests
475 Übungsfragen
(abgestimmt auf die offiziellen Prüfungsziele)
Zertifizierung
GCPMLE.AE1
Google Zertifizierung
Bereit für die Prüfung?
Fügen Sie Ihren offiziellen GCPMLE.AE1 Prüfungsgutschein zu Ihrer Bestellung hinzu.
Offizieller Gutschein · Schnelle Lieferung · Wiederholungspaket verfügbar
Der Prüfungsgutschein wird separat verkauft und ist im Standardkurs nicht enthalten.
04 / FAQS
Fragen, bevor Sie beginnen
What is the Google Cloud Certified Professional Machine Learning Engineer certification?+
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.
Who should take this certification online course?+
Anyone aiming to master ML on Google Cloud — data scientists, ML engineers, software developers, and even tech enthusiasts looking to improve their career.
What are the prerequisites for the course?+
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.
What is the format of the Google Cloud ML Engineer certification exam?+
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.
How much does the certification exam cost?+
The machine learning engineer certification costs $200 USD.
What job roles can I pursue after completing this online course?+
You’ll be ready for roles like Machine Learning Engineer, Data Scientist, Cloud Architect, and other AI-driven positions across top tech companies.
Wie hoch ist die Prüfungsgebühr?+
200 USD (zuzüglich Steuern, falls zutreffend)
Wo kann ich die Prüfung ablegen?+
Google
Wie ist die Prüfung aufgebaut?+
Multiple-Choice- und Multiple-Select-Fragen
Wie viele Fragen umfasst die Prüfung?+
Die Prüfung umfasst 50-60 Fragen.
Wie lange dauert die Prüfung?+
120 Minuten
Wie lauten die Regelungen zur Wiederholung der Prüfung?+
Hier sind die Richtlinien für Wiederholungsaufnahmen:
- Cloud Digital Leader: Sie haben maximal zehn Versuche innerhalb eines Jahres und müssen zwischen jedem fehlgeschlagenen Versuch mindestens 14 Tage warten.
- Prüfungen für die Associate- und Professional-Zertifizierung: Sie haben maximal vier Versuche innerhalb von zwei Jahren. Sollten Sie die Prüfung nicht bestehen, können Sie sie nach 14 Tagen wiederholen. Bestehen Sie auch den zweiten Versuch nicht, müssen Sie 60 Tage warten, bevor Sie die Prüfung ein drittes Mal ablegen können. Bestehen Sie auch den dritten Versuch nicht, müssen Sie 365 Tage warten, bevor Sie die Prüfung ein viertes Mal ablegen können.
Wo finde ich weitere Informationen zu dieser Prüfung?+
Keep going
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Prepare for Google Cloud ML Certification
Think big & train smart to become the future of machine learning with Google Cloud!
- 1 Jahr voller Zugang
- 11 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt