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Career Bonuses
The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.
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How much Professional Machine Learning Engineer - Google Cost
The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Defining problem type (classification, regression, clustering, etc.)
- Identifying data sources
- Assessing data readiness
- Success metrics
- Managing incorrect results
- Define ML problem
- Defining business problems
- Defining outcome of model predictions
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining output use
- Determination of when a model is deemed unsuccessful
- Key results
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Defining the input (features) and predicted output format
- Identifying nonML solutions
- Define business success criteria
- Assessing and communicating business impact
- Assessing ML solution readiness
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Optimize model performance and generalization - Work with foundation models and generative AI techniques - Design and run experiments |
| Topic 2: Architect low-code AI solutions | 12% | - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs |
| Topic 3: Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Address data privacy, compliance, and governance |
| Topic 4: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments |
| Topic 5: Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Implement CI/CD for ML systems - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Topic 6: Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health |
