Professional Machine Learning Engineer

A Professional Machine Learning Engineer creates, evaluates, and optimizes AI solutions using Google Cloud capabilities and knowledge of conventional ML approaches, bringing these solutions to production readiness. They work with large, complex datasets and create repeatable, reusable code. The ML Engineer designs and operationalizes generative AI solutions based on base models. The ML Engineer considers best practices for responsible AI use and works closely with other job roles to ensure the long-term success of AI-based applications. The ML Engineer has extensive programming skills with data platforms and distributed data processing tools. The ML Engineer is familiar with model architecture, data and ML pipeline creation, generative AI, and metric interpretation. They are familiar with the fundamental concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer empowers teams across the organization to leverage AI solutions. Through training, retraining, deployment, planning, monitoring, and improving models, the ML Engineer designs and builds scalable, high-performance solutions.

* Note: This exam does not test programming skills per se. If you have a basic understanding of Python and Cloud SQL, you should be able to interpret all questions using code snippets.

The Google Cloud Professional Machine Learning Engineer certification exam covers the following topics:

• Developing low-code AI solutions
• Collaborating within and across teams to manage data and models
• Scaling prototypes into ML models
• Deploying and scaling models
• Automating and orchestrating ML pipelines
• Monitoring AI solutions

This version of the Professional Machine Learning Engineer exam includes tasks related to generative AI, including developing AI solutions with Model Garden and Vertex AI Agent Builder, and evaluating generative AI solutions.