Why Choose PRAGYASHAL
We're dedicated to empowering ML professionals with unparalleled advantages.
100% Pass Guarantee
We're so confident in our training methodology that we offer a full pass guarantee for the Professional Machine Learning Engineer exam.
Masterful Curriculum
Our course content precisely reflects the official Google Cloud exam guide, ensuring comprehensive coverage.
Expert Guidance
Learn from industry veterans and certified professionals with deep practical experience in Google Cloud ML.
Realistic Mock Tests
Multiple full-length mock examinations designed to simulate the actual exam format and difficulty.
Exam Voucher Included
Your course fee includes the official Google Cloud exam voucher ($200 value) - streamlined process.
Global Recognition
Earn a prestigious Google Cloud certification that enhances your professional profile worldwide.
What You'll Achieve
Architect Low-Code AI Solutions
Develop ML models using BigQuery ML, ML APIs, and AutoML.
Collaborate & Manage Data/Models
Explore, preprocess, and manage organization-wide data and models in Vertex AI.
Scale Prototypes into ML Models
Build and train scalable models, choosing appropriate hardware for optimal performance.
Serve & Scale Models
Implement batch and online inference, and scale model serving efficiently.
Automate & Orchestrate ML Pipelines
Develop end-to-end ML pipelines and automate model retraining with CI/CD.
Who Is This For
Ideal for experienced ML practitioners and engineers seeking to optimize AI solutions on Google Cloud.
ML Engineers
Professionals building, evaluating, and productionizing AI solutions.
Data Scientists
Scientists looking to operationalize ML models and scale prototypes.
AI/ML Architects
Architects designing and optimizing scalable AI solutions.
Experienced Developers
Developers with 3+ years industry and 1+ year Google Cloud ML experience.
Comprehensive Training Program
Our structured course aligns perfectly with the official exam guide.
Architecting Low-Code AI Solutions
~13% of exam- Developing ML models using BigQuery ML
- Building AI solutions with ML APIs/Foundation Models
- Training models using AutoML (tabular, text, speech, images)
- Configuring and debugging trained models
Collaborating to Manage Data & Models
~14% of exam- Exploring & preprocessing organization-wide data
- Managing datasets in Vertex AI Feature Store
- Model prototyping using Jupyter notebooks (Vertex AI Workbench)
- Tracking and running ML experiments (Vertex AI Experiments, TensorBoard)
Scaling Prototypes into ML Models
~18% of exam- Building and training models (frameworks, architecture, data organization)
- Distributed training and hyperparameter tuning
- Troubleshooting ML model training failures
- Choosing appropriate hardware for training (CPU, GPU, TPU)
Serving and Scaling Models
~20% of exam- Batch and online inference (Vertex AI, Dataflow, BigQuery ML)
- Using different frameworks to serve models (PyTorch, XGBoost)
- Organizing a model registry and A/B testing
- Scaling online model serving (Vertex AI Prediction, containerized serving)
Automating & Orchestrating ML Pipelines
~22% of exam- Developing end-to-end ML pipelines (Vertex AI Pipelines, Cloud Composer)
- Automating model retraining (CI/CD model deployment)
- Tracking and auditing metadata (Vertex AI Experiments, Vertex ML Metadata)
- Ensuring consistent data preprocessing between training and serving
Monitoring AI Solutions
~13% of exam- Identifying risks to AI solutions (secure AI systems, responsible AI)
- Monitoring, testing, & troubleshooting AI solutions
- Establishing continuous evaluation metrics (Vertex AI Model Monitoring)
- Monitoring for training-serving skew & feature attribution drift
What's Included
Live Sessions
Expert-led interactive training with real-time Q&A
Study Materials
Comprehensive notes, presentations & resources
Exam Voucher
Official Google Cloud exam voucher included
Mock Tests
Multiple full-length realistic practice exams
Doubt Clearing
Dedicated sessions for personalized guidance
Use Cases
Practical workshops & real-world discussions
Lifetime Access
Perpetual access to all course materials
Student Support
Dedicated support throughout your journey
Earn Your Official Certification
Google Cloud Exam Details
Professional Machine Learning Engineer Certification
Length
Two hours
Format
50-60 multiple choice and multiple select questions
Language
English
Delivery Method
Online-proctored or onsite-proctored
Validity
Two years
Recommended Experience
3+ years industry, 1+ year Google Cloud ML
Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.
