Professional Machine Learning Engineer
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.
The Professional Machine Learning Engineer exam assesses your ability to:
This exam is available in English.
About this certification exam
- Length: Two hours
- Registration fee: $200 (plus tax where applicable)
- Language: English
- Exam format: Multiple choice and multiple select
- Exam Delivery Method:
- Prerequisites: None
- Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
Step 1: Get real world experience
Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years hands-on experience with Google Cloud products and solutions. Ready to start building? Explore the Google Cloud Free Tier for free usage (up to monthly limits) of select products.
Step 2: Understand what's on the exam
The exam guide contains a complete list of topics that may be included on the exam. Review the exam guide to determine if your skills align with the topics on the exam.
Step 3: Review the sample questions
Familiarize yourself with the format of exam questions and example content that may be covered on the Machine Learning Engineer exam.
Step 4: Round out your skills with training
If you are looking to structure preparation for the exam with additional training, here are some options to explore either as online self-paced training, in-person classroom training, or hands-on labs practice.
Explore self-based on demand courses offered via Coursera, Pluralsight, and Qwiklabs. Complete the recommended curriculum:
Broaden your knowledge with additional self-paced labs and quests:
Get extra practice with Google Cloud through self-paced exercises covering a single topic or theme offered via Qwiklabs.
Complete the recommended quests and labs:
Explore Google Cloud documentation for in-depth discussions on the concepts and critical components of Google Cloud.
Learn how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on Google Cloud with hands-on guide for developers entering the data science field: Data Science on Google Cloud Platform
Step 5: Schedule an exam
Register and select the option to take the exam remotely or at a nearby testing center.