• Certification Path
Libraries: This path is only available in the libraries listed. To access this path, purchase a license for the corresponding library.
• Cloud
• Data

**Note:** this learning path is actively in production. More content will be added to this page as it publishes and becomes available in the library. The planned content for this path includes the following: - Architect low-code AI solutions - Collaborate within and across teams to manage data and models - Scale prototypes into ML models - Serve and scale models - Automate and orchestrate ML pipelines - Monitor AI solutions

A **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 foundational 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 the areas of model architecture, data and ML pipeline creation, generative AI, and metrics interpretation. The ML Engineer is familiar with 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.