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
Before attempting the Machine Learning Engineer exam, it's recommended that you have 3+ years hands-on experience with Google Cloud products and solutions.
Exams and recommended training
- Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM)
- Machine Learning with TensorFlow on Google Cloud Platform (MLTF)
About this certification exam:
- Length: Two hours
- Language: English
- Exam format: Multiple choice and multiple select
- Exam Delivery Method:
- Take the online-proctored exam from a remote location, review the online testing requirements.
- 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.