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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
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| Section | Weight | Objectives |
|---|---|---|
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools - Automate retraining and model updates |
| Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Design and run experiments - Optimize model performance and generalization - Select appropriate model architectures and frameworks |
| Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Troubleshoot and maintain production systems |
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns |
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
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