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 Laboratorios prácticos — nothing to install
- 15 Lecciones interactivas y 105 topics mapped to the official exam objectives
- 475 Preguntas del examen de práctica y 2 Pruebas completas
Expert A tu propio ritmo · 1 año de acceso
11 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
15Lecciones interactivas
105Topics
11Laboratorio en vivo
475Preguntas del examen de práctica
70Tarjetas didácticas
70Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
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 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
11 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
-
475 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
-
1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
15 Lecciones interactivas · 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 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
04 Feature Engineering 8 topics · 2 Laboratorio en vivo +
- Consistent Data Preprocessing
- Encoding Structured Data Types
- Class Imbalance
- Feature Crosses
- TensorFlow Transform
- GCP Data and ETL Tools
- Summary
- Exam Essentials
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Choosing the Right ML Infrastructure 7 topics · 1 Laboratorio en vivo +
- Pretrained vs. AutoML vs. Custom Models
- Pretrained Models
- AutoML
- Custom Training
- Provisioning for Predictions
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Architecting ML Solutions 7 topics · 1 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
07 Building Secure ML Pipelines 5 topics · 1 Laboratorio en vivo +
- Building Secure ML Systems
- Identity and Access Management
- Privacy Implications of Data Usage and Collection
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
08 Model Building 8 topics · 2 Laboratorio en vivo +
- 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 Laboratorio en vivo 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 Laboratorio en vivo +
- MLOps Maturity
- Retraining and Versioning Models
- Feature Store
- Vertex AI Permissions Model
- Common Training and Serving Errors
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
15 BigQuery ML 8 topics · 1 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
11 Laboratorio en vivos- 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
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Detalles del examen
Google Cloud Certified Professional Machine Learning Engineer Detalles
El curso de Ingeniero de Aprendizaje Automático Profesional de Google Cloud te proporciona las habilidades para diseñar, construir e implementar modelos sofisticados de aprendizaje automático en Google Cloud. Profundizarás en temas clave como la definición de problemas de ML, la arquitectura de soluciones de ML escalables, el desarrollo y la optimización de modelos, la automatización de pipelines de ML de extremo a extremo y la supervisión del rendimiento del modelo. Este curso es ideal para usuarios experimentados de Google Cloud que desean llevar sus habilidades de aprendizaje automático al siguiente nivel.
Questions
50-60
(por examen)
Duration
120 minutos
(por examen)
Exam Fee
USD 200 (plus taxes where applicable)
(por examen)
Delivered by
Google
(presencial u online)
Question Format
Multiple-choice and Multiple-select questions
opción única/múltiple, basado en el desempeño
Exámenes de práctica
475 Preguntas de práctica
(alineado con los objetivos oficiales del examen)
Certificación
GCPMLE.AE1
Google credencial
¿Listo para presentar el examen?
Agrega tu GCPMLE.AE1 bono de examen oficial a tu pedido.
Bono oficial · Entrega rápida · Paquete de repetición disponible
El bono de examen se vende por separado y no está incluido en el curso estándar.
04 / Preguntas frecuentes
Preguntas antes de empezar
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.
¿Cuál es la cuota de inscripción al examen?+
200 USD (más impuestos, si corresponde)
¿Dónde puedo hacer el examen?+
Google
¿Cuál es el formato del examen?+
Preguntas de opción múltiple y de selección múltiple
¿Cuántas preguntas se hacen en el examen?+
El examen consta de entre 50 y 60 preguntas.
¿Cuál es la duración del examen?+
120 minutos
¿Cuál es la política de repetición del examen?+
Estas son las políticas para repetir el examen:
- Líder Digital en la Nube: dispone de un máximo de diez intentos en un período de un año y debe esperar al menos 14 días entre cada intento fallido.
- Exámenes de certificación de Asociado y Profesional: dispone de un máximo de cuatro intentos en dos años. Si no aprueba el examen, puede volver a presentarse después de 14 días. Si no lo aprueba la segunda vez, deberá esperar 60 días antes de presentarse por tercera vez. Si no lo aprueba la tercera vez, deberá esperar 365 días antes de presentarse por cuarta vez.
¿Dónde puedo encontrar más información sobre este examen?+
Keep going
You might also like
Prepare for Google Cloud ML Certification
Think big & train smart to become the future of machine learning with Google Cloud!
- 1 año de acceso completo
- 11 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito