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:
- Managing incorrect results
- Assessing data readiness
- Success metrics
- Define ML problem
- Defining the input (features) and predicted output format
- Identifying nonML solutions
- Defining problem type (classification, regression, clustering, etc.)
- Assessing and communicating business impact
- Defining business problems
- Identifying data sources
- Defining outcome of model predictions
- Aligning with Google AI principles and practices (e.g. different biases)
- Key results
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Assessing ML solution readiness
- Defining output use
- Define business success criteria
- Determination of when a model is deemed unsuccessful
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
Design pipeline. Considerations include:
- Use CI/CD to test and deploy models
- Decoupling components with Cloud Build
- Model binary options
- Testing for target performance
- A/B and canary testing
- Track and audit metadata
- Google Cloud serving options
- Storing data and generated artifacts
- Setup of trigger and pipeline schedule
- Identification of components, parameters, triggers, and compute needs
- Tuning compute performance
- Hooking models into existing CI/CD deployment system
- Hybrid or multi-cloud strategies
- Organization and tracking experiments and pipeline runs
- Constructing and testing of parameterized pipeline definition in SDK
- Model/dataset lineage
- Implement training pipeline
- Hooking into model and dataset versioning
- Implement serving pipeline
- Performing data validation
- Orchestration framework
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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