Still think Vertex AI is just “the Google version of SageMaker”? The Professional Machine Learning Engineer exam expects hands-on judgment, not just vocabulary. This is the course that gets you there.
Learn Vertex AI, MLOps automation, and GenAI on Google Cloud the way the actual PMLE exam tests it — ending in a certification-ready capstone, not just a vocabulary list.
By the time you finish this course, you will have built and deployed a real ML or RAG solution on Vertex AI — complete with a pipeline and monitoring — and sat two full-length timed mock exams covering all six PMLE domains. This course is how you get there in 8 weeks, building on AWS AI Practitioner Prep rather than starting from zero.
Syllabus
Phase 1: ML Solution Architecture & Data Engineering (Weeks 1–4)
Modules
Solution Architecture & Vertex AI Basics
Data Engineering & Feature Management
Model Development
Responsible AI & GenAI on Vertex AI
Phase 2: MLOps, Deployment & Monitoring (Weeks 5–8)
Modules
Serving & Pipelines
MLOps Automation & Monitoring
Troubleshooting & Capstone Kickoff
Capstone & Certification Readiness
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to
- Frame a business problem as an ML problem and choose the right Google Cloud service (AutoML, BigQuery ML, custom training)
- Engineer features and manage data with BigQuery, Dataflow, and Vertex AI Feature Store
- Train, tune, and evaluate models with Vertex AI, including hardware selection (CPU/GPU/TPU)
- Build RAG and GenAI solutions with Model Garden and Vertex AI Agent Builder
- Deploy models to production with autoscaling, traffic splitting, and CI/CD pipelines
- Monitor for data drift and training-serving skew
- Complete two full-length, timed mock exams covering all six PMLE domains
- Deliver a capstone project: a deployed, monitored ML or RAG solution on Vertex AI
- Career roles you'll be ready for: ML Engineer, Cloud AI Engineer, MLOps Engineer (GCP)
Tools
Vertex AI
BigQuery ML
Dataflow
Kubeflow Pipelines
Cloud Build
Vertex AI Agent Builder
Python
Who Should Enrol
Already through AWS AI Practitioner Prep?
This course assumes AI/ML fundamentals and moves straight into GCP-specific, hands-on territory.
ML/data professional targeting a GCP-first employer?
Every concept is taught with a working lab on Vertex AI — you'll leave with a deployed capstone, not just exam vocabulary.
Comfortable with Python and SQL, new to Google Cloud specifically?
Phase 1 gets you oriented on Vertex AI fast so you can focus on the parts of the exam that are genuinely new.
Considering the full Cloud AI Certification Prep track?
This is Course 2 of 2. Complete AWS AI Practitioner Prep first if you haven't — this course assumes that foundation.
Market Growth
AI Industry Growth
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Mid-Level AI Salaries
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Senior AI Compensation
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AI/ML Job Openings by 2026
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FAQs
AI/ML professionals and AWS AI Practitioner Prep graduates who want a hands-on, production-grade Google Cloud ML credential.
It’s recommended. This course assumes AI/ML fundamentals and moves quickly into GCP-specific, hands-on content — it doesn’t re-teach the basics.
No, and that’s worth knowing before you enrol. Google recommends 3+ years of industry experience for PMLE, including hands-on GCP work. This course is built for people ready for that jump, not absolute beginners.
A capstone project: a real ML or RAG solution built, deployed, and monitored on Vertex AI, plus two full-length timed mock exams across all six PMLE domains.
moventAI selected GCP as the primary second-cloud platform for this track. If your organization runs on Azure, talk to us — we can discuss a custom cohort.