Professional Machine Learning Engineers leverage Google Cloud technologies to build, evaluate, productionize, and optimize ML models. They handle large datasets and create code that's both reusable and scalable, emphasizing responsible AI and fairness. These engineers possess strong programming skills, experience with data platforms, and proficiency in model architecture, data and ML pipelines, and metrics interpretation. They are versed in MLOps, application development, infrastructure management, data engineering, and data governance, aiming to make ML accessible across the organization. The certification exam covers: - Architecting low-code ML solutions - Collaborating to manage data and models - Scaling prototypes into ML models - Serving and scaling models - Automating and orchestrating ML pipelines - Monitoring ML solutions - Exam Length: 2 hours - Registration Fee: $200 (plus applicable taxes) - Language: English - Exam Format: 50-60 multiple choice and select questions - Delivery Method: Online or onsite-proctored exam - Prerequisites: None, though 3+ years of industry experience and 1+ year with Google Cloud is recommended. - Recertification: Required every two years to maintain certification status.