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To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
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.
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Design and run experiments - Optimize model performance and generalization - Work with foundation models and generative AI techniques |
| Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
| Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Monitor model performance, fairness, and drift |
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
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