About The Course

A Professional Machine Learning Engineer designs builds, and productions 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 the 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
• Architect ML solutions
• Design data preparation and processing systems
• Develop ML models
• Automate & orchestrate ML pipelines
• Monitor, optimize, and maintain ML solutions