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:

  • Frame ML problems
  • Architect ML solutions
  • Prepare and process data
  • Develop ML models
  • Automate & orchestrate ML pipelines
  • Monitor, optimize, and maintain ML solutions

    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:
      1. Take the online-proctored exam from a remote location, review the online testing requirements.
      2. 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+ 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.

      See Google Cloud Free Tier  

    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.

      See exam guide  

    Machine Learning Engineer Prep Webinar

    Join Googlers and recently-certified experts for tips and insights on data processing systems, machine learning models, solution quality, and more.

    Register now

    Step 3: Review the sample questions

    Step 4: Round out your skills with training

    Step 5: Schedule an exam