What Skills Will You Gain
Architecting low-code ML solutions
Collaborating within and across teams to manage data and models
Scaling prototypes into ML models
Serving and scaling models in production
1703 Learners$69.95
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$69.95
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$65.95
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$55.95
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The Google Cloud Certified Professional Machine Learning Engineer course covers designing, building, and productionizing ML models on Google Cloud. You'll work through structured video lessons and exam-style practice questions that mirror the real ML Engineer exam, building the practical judgment needed to apply these skills on the job. By the end, you'll be ready to sit the ML Engineer exam and earn the Google Cloud Certified Professional Machine Learning Engineer credential. This Google Cloud Certified Professional Machine Learning Engineer training pairs guided lessons with a full Google Cloud Certified Professional Machine Learning Engineer practice test, so you walk into the ML Engineer exam fully prepared.
Whether you're brand new to this area or brushing up before the ML Engineer exam, this course paces the material so nothing feels rushed.
Architecting low-code ML solutions
Collaborating within and across teams to manage data and models
Scaling prototypes into ML models
Serving and scaling models in production
24x7 support from Google-certified experts
This course is valid for 2 years from the date of purchase
Practice tests with unique, exam-style questions
Detailed explanations for every practice question
Hands-on labs using real Google services
Downloadable ML Engineer cheat sheet for quick revision
Official domain-by-domain weightings for the ML Engineer exam were not included in the source data for this page, so the skill areas below are listed without percentage breakdowns. Verify current weightings on the vendor's official Google Cloud Certified Professional Machine Learning Engineer exam guide before publishing.
Architecting low-code ML solutions
Collaborating within and across teams to manage data and models
Scaling prototypes into ML models
Serving and scaling models in production
The Google Cloud Certified Professional Machine Learning Engineer course lays out a clear path to the ML Engineer exam, so you're not guessing what to study next.
Start with the video lessons in this Google Cloud Certified Professional Machine Learning Engineer course to build a working understanding of the core concepts the ML Engineer exam covers. Then move into the hands-on labs to apply what you've learned in a real environment instead of just reading about it.
Next, work through the ML Engineer practice test set. Each Google Cloud Certified Professional Machine Learning Engineer practice test question comes with a detailed explanation, so a wrong pick becomes a lesson you actually retain.
When your ML Engineer practice test scores stay consistently above the passing mark, you're ready to book the Google Cloud Certified Professional Machine Learning Engineer exam. For a fuller walkthrough, refer to a dedicated Google Cloud Certified Professional Machine Learning Engineer exam preparation guide alongside this course.
The ML Engineer certification has no mandatory prior certification requirement.
Once you pass, the Google Cloud Certified Professional Machine Learning Engineer certification stays valid for 3 Years. Before it expires, you can renew the ML Engineer certification by retaking the current exam.
Many learners treat the Google Cloud Certified Professional Machine Learning Engineer certification as a step within a broader vendor certification track. Check Whizlabs' related course listings for a logical next-level credential once you've completed this ML Engineer course.
This certification validates your ability to work with designing, building, and productionizing ML models on Google Cloud. It signals to employers that you can apply these skills in real Google projects, supporting stronger roles, responsibilities, and career growth.
Positions you for machine learning engineers and data scientists roles, and signals proven, job-ready expertise in designing, building, and productionizing ML models on Google Cloud.
Helps your organization apply designing, building, and productionizing ML models on Google Cloud more effectively, reducing risk and improving efficiency in real deployments.
Deepens your hands-on understanding of designing, building, and productionizing ML models on Google Cloud, moving beyond theory into practical, job-ready skills.
Professional-level certifications like this are consistently linked to stronger salaries and career growth in Google-related roles.
Exam Format and Information
Prior Certification
Not Required
Exam Validity
3 Years
Exam Fee
200 USD
Exam Duration
120 Minutes
No. of Questions
50-60 Qs
Passing Score
Pass/Fail
Best for those with hands-on or working familiarity with designing, building, and productionizing ML models on Google.
English.
Multiple choice and multiple select questions.
There are no formal prerequisites for this exam. Hands-on experience with designing, building, and productionizing ML models on Google Cloud makes it more approachable. It's designed for machine learning engineers, data scientists, and anyone building these skills.
Machine learning engineers working with designing, building, and.
Data scientists preparing for professional-level certification.
IT professionals expanding into this specialization.
Consultants advising clients on Google best practices.
Students and career changers entering the field.
Anyone pursuing the ML Engineer credential.
In this course, you will get unlimited access to 2 practice tests with 55 unique questions in each and 1 free test with 15 questions. All these questions are similar to the actual GCP Professional Machine Learning Engineer certification exam questions and can be treated as mock exams. Practice for the actual certification exam by taking up these practice tests today.
Get help from Google-certified trainers whenever you're stuck on a concept or question.
Practice with real Google services in guided labs that mirror the exam and the job.
Unique ML Engineer style questions with detailed explanations from subject matter experts.
Explore live Google services in a ready-to-use sandbox environment.
Testimonials
Overall Feedbacks
This course is ideal for:
Data professional
Cloud engineers
People studying for the Google Professional Machine Learning Engineer exam
Undoubtedly, the Google Cloud Certified Professional Machine Learning Engineer certification from GCP is the best cloud certification for machine learning.
If you want to take this Professional Machine Learning Engineer course and become a Google Certified Professional Machine Learning Engineer, you need to have basic understanding of cloud concepts and coding in Python.
Yes, Google is one of the pioneers in using machine learning and cutting-edge technologies to solve several real-world challenges. So they hire machine learning engineers for various projects.
According to Google, you must have these minimum requirements to become a machine learning engineer at Google: Bachelor’s degree or equivalent practical experience, 3 years of software development experience, or 1 year with a relevant advanced degree, experience in applied machine learning or artificial intelligence, experience with one or more general-purpose programming languages including but not limited to: Java, C/C++, or Python.
4.9/5
4.8/5
Mohamed ASSELLAOU
Mohamed ASSELLAOU
