Google Professional Machine Learning Engineer Exam Preparation (preview)

AVG Duration13h
Course Created with Sketch. 8 Resources Created with Sketch. 1 Labs Created with Sketch. 4


******** PREVIEW *******

This learning path is still in development and requires additional content to cover all of the points assessed in the exam. We are busy working on new content to fill the gaps. Until then, the courses and labs currently available in this Learning Path will allow you to get started in preparing for this certification.


This learning path is designed to help you prepare for the Google Certified Professional Machine Learning Engineer exam. Even if you don't plan to take the exam, these courses will help you gain a solid understanding of how to implement machine learning on Google Cloud Platform. Candidates who pass the exam will earn the Google Professional Machine Learning Engineer certification.

The Professional Machine Learning Engineer exam tests your knowledge of six subject areas.

Learning Objectives

  • Frame machine learning problems
  • Design a machine learning solution architecture
  • Prepare and process data
  • Develop machine learning models
  • Automate and orchestrate machine learning pipelines
  • Monitor, optimize, and maintain machine learning solutions

Intended Audience

  • Data professionals
  • People studying for the Google Professional Machine Learning Engineer exam


  • Basic understanding of cloud concepts
  • Experience writing Python code


If you have thoughts or suggestions on this learning path, please contact Cloud Academy at


Your certificate for this learning path

Learning Path Steps


In this course, you'll learn about GCP services such as compute, storage, and networking, and how to create virtual machines and web apps using the Google Cloud Console and gcloud CLI.


This course covers the foundations and history of machine learning as well as the principles of memory storage, computing power, and phone/web applications.


In this course, you'll learn how to train and deploy neural networks with Google AI Platform.


Learn how to build a CNN, train it on Machine Learning Engine and visualize its performance. Learn how to recognize overfitting and apply different methods to avoid it.


In this course, you'll learn how to use recurrent neural networks to train more complex models.


In this course, you'll learn how to improve the performance of your neural networks with this learning path.


In this lab, you will inspect data stored in Cloud Storage and understand the sensitive information therein.


Learn how to load data into BigQuery, run queries using standard SQL, and export data from BigQuery with this hands-on course.


This Lab will show you the basic concepts of BigQuery and will allow you to handle data and query them in a real GCP environment.


This lab will explain some of the basic concepts of BigQuery ML along with an example of training a linear regression and binary logistic regression model.


This lab is aimed at data visualization beginners who want to understand how to import data and make their first insight using Google Data Studio.


In this course, you'll learn how to write data processing programs using Apache Beam and run them using Cloud Dataflow, as well as learning how to run both batch and streaming jobs.


Recommended Reading for Google Professional Machine Learning Engineer Exam

About the Author
Learning paths76

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).