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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.
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:
- Assessing and communicating business impact
- Defining output use
- Assessing ML solution readiness
- Defining the input (features) and predicted output format
- Determination of when a model is deemed unsuccessful
- Key results
- Identifying data sources
- Managing incorrect results
- Assessing data readiness
- Identifying nonML solutions
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining business problems
- Defining problem type (classification, regression, clustering, etc.)
- Success metrics
- Define business success criteria
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Defining outcome of model predictions
- Define ML problem
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Select appropriate model architectures and frameworks - Design and run experiments - Work with foundation models and generative AI techniques |
| Monitor and optimize AI solutions | 16% | - Troubleshoot and maintain production systems - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift - Optimize cost, latency, and resource usage |
| Collaborate to manage data and models | 16% | - Organize and prepare enterprise data
- Manage datasets and features in Vertex AI |
| 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 |
| Train and deploy models | 20% | - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction |
| Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Implement CI/CD for ML systems - Design end-to-end ML workflows - Use Vertex AI Pipelines, TFX, and other orchestration tools |
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