Google Professional Machine Learning Engineer

Domain 1: Architect Low-Code AI Solutions

• Identify and design AI solutions using Google Cloud services.
• Use pre-trained APIs and AutoML capabilities.
• Select appropriate Google Cloud AI services.

Domain 2: Design and Prototype ML Solutions

• Frame business problems as ML problems.
• Select datasets and features.
• Develop and validate ML prototypes.

Domain 3: Develop ML Models

• Build and train ML models.
• Feature engineering and model optimization.
• Evaluate and improve model performance.

Domain 4: Orchestrate and Automate ML Pipelines

• Design and implement ML pipelines.
• Automate training and deployment workflows.
• Use Vertex AI Pipelines and MLOps practices.

Domain 5: Deploy and Scale ML Solutions

• Deploy models to production environments.
• Monitor model performance and drift.
• Optimize scalability, reliability, and cost.

Domain 6: Monitor, Optimize, and Maintain ML Solutions

• Monitor operational health and model performance.
• Manage retraining strategies.
• Implement governance and responsible AI practices.

Training Eligibility

• There are no mandatory prerequisites to attend the Google Professional Machine Learning Engineer training program.
• Prior experience with Machine Learning concepts and Google Cloud is recommended.

Recommended Knowledge

• Machine Learning fundamentals.
• Data Science concepts.
• Programming using Python.
• Cloud computing concepts.
• Google Cloud services and infrastructure.
• Hands-on experience with Machine Learning projects is beneficial.