A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.
*Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.
The Professional Machine Learning Engineer exam assesses your ability to:
- Architect low-code ML solutions
- Scale prototypes into ML models
- Automate and orchestrate ML pipelines
- Collaborate within and across teams to manage data and models
- Serve and scale models
- Monitor ML solutions
Prerequisites
Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years of industry experience, including 1 or more years designing and managing solutions using Google Cloud.
Recommended training for this certification
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Exams
- Length: Two hours
- Registration fee: $200 (plus tax where applicable)
- Language: English
- Exam format: 50-60 multiple choice and multiple select questions
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.
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
Step 5: Schedule an exam
Register and select the option to take the exam remotely or at a nearby testing center.
Recertification
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.