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 considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
The Professional Machine Learning Engineer exam assesses your ability to:
- Frame ML problems
- Develop ML models
- Architect ML solutions
- Automate and orchestrate ML pipelines
- Design data preparation and processing systems
- Monitor, optimize, and maintain ML solutions
About this certification exam
Length: Two hours
Registration fee: $200 (plus tax where applicable)
Language: English
Exam format: 50-60 multiple choice and multiple select questions
Exam Delivery Method:
a. Take the online-proctored exam from a remote location, review the online testing requirements
b. Take the onsite-proctored exam at a testing center, locate a test center near you
Prerequisites: None
Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.
Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. 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.
Exam overview
Step 1: Get real world experience
Before attempting the Machine Learning Engineer exam,
it's recommended that you have 3+ years of 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 questions and
example content that may be covered on the Machine
Learning Engineer exam.
Step 4: Round out your skills with training
Prepare for the exam by following the Machine
Learning Engineer learning path. Explore online
training, in-person classes, hands-on labs, and
other resources from Google Cloud.
Prepare for the exam with Googlers and certified
experts. Get valuable exam tips and tricks, as well
as insights from industry experts.
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 guides 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.
Review exam
terms and conditions
and
data sharing policies.