Objectives:

• Describe how to develop and implement machine learning solutions using low-code tools and services on Google Cloud
• Explain how to effectively manage data, prototype models, and collaborate within and across teams to build robust ML solutions.
• Determine how to scale ML prototypes into production-ready models by selecting appropriate frameworks, training effectively, and choosing optimal hardware.
• Describe how to deploy and scale ML models in production using various serving strategies and infrastructure on Google Cloud.
• Explain how to automate and orchestrate end-to-end ML pipelines to streamline model development, deployment, and retraining.
• Identify the key tasks and considerations for monitoring, testing, and troubleshooting ML solutions to ensure performance, reliability, and responsible AI practices.

The Professional Machine Learning Engineer exam assesses knowledge in six areas:

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