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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:
- Key results
- Managing incorrect results
- Success metrics
- Defining the input (features) and predicted output format
- Defining outcome of model predictions
- Identifying nonML solutions
- Assessing and communicating business impact
- Define business success criteria
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Define ML problem
- Defining business problems
- Defining output use
- Assessing ML solution readiness
- Determination of when a model is deemed unsuccessful
- Assessing data readiness
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining problem type (classification, regression, clustering, etc.)
- Identifying data sources
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
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance
- Manage datasets and features in Vertex AI
- Organize and prepare enterprise data
- 1. Work with structured, unstructured, and semi-structured data
- 2. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools
|
| Topic 2: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction
- Configure training jobs and environments
- Implement generative AI deployment patterns
- Use Vertex AI deployment features and infrastructure
|
| Topic 3: 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
|
| Topic 4: Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks
- Work with foundation models and generative AI techniques
- Design and run experiments
- Optimize model performance and generalization
|
| Topic 5: 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
|
| Topic 6: 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
|