PDF VersionUnderstanding 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 ML solution readiness
- Define business success criteria
- Identifying nonML solutions
- Defining business problems
- Defining output use
- Assessing data readiness
- Identifying data sources
- Success metrics
- Defining problem type (classification, regression, clustering, etc.)
- Managing incorrect results
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining outcome of model predictions
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Define ML problem
- Assessing and communicating business impact
- Determination of when a model is deemed unsuccessful
- Key results
- Defining the input (features) and predicted output format
The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
| Topic 2: Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health |
| Topic 3: 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 |
| Topic 4: Automate and orchestrate ML pipelines | 18% | - Use Vertex AI Pipelines, TFX, and other orchestration tools - Automate retraining and model updates - Implement CI/CD for ML systems - Design end-to-end ML workflows |
| Topic 5: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 6: Train and deploy models | 20% | - Implement generative AI deployment patterns - Deploy models for online, batch, and streaming prediction - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure |
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