Google Cloud Professional Machine Learning Engineer

About this course

Google Cloud Professional Machine Learning Engineer CertificateA Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes AI solutions by using Google Cloud capabilities and knowledge of conventional ML approaches. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer designs and operationalizes generative AI solutions based on foundation models. The ML Engineer considers responsible AI practices and collaborates closely with other job roles to ensure the long-term success of AI-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 areas such as model architecture, data, and ML pipeline creation, as well as generative AI and metrics interpretation. The ML Engineer is familiar with the foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer enables teams across the organization to use AI solutions. 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 skills. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.This version of the Professional Machine Learning Engineer exam covers tasks related to generative AI, including building AI solutions using Model Garden and Vertex AI Agent Builder, and evaluating generative AI solutions.

Course objectives

• prepare for the Google Cloud Professional Machine Learning Engineer certification
• develop a strong grasp of responsible AI practices
• learn to use Google Cloud's AI capabilities effectively