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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
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Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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:
- Model binary options
- Hooking into model and dataset versioning
- Implement serving pipeline
- Orchestration framework
- Setup of trigger and pipeline schedule
- Use CI/CD to test and deploy models
- Performing data validation
- Tuning compute performance
- Track and audit metadata
- Hybrid or multi-cloud strategies
- Google Cloud serving options
- Decoupling components with Cloud Build
- A/B and canary testing
- Organization and tracking experiments and pipeline runs
- Model/dataset lineage
- Storing data and generated artifacts
- Identification of components, parameters, triggers, and compute needs
- Constructing and testing of parameterized pipeline definition in SDK
- Hooking models into existing CI/CD deployment system
- Testing for target performance
- Implement training pipeline
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 business problems
- Identifying nonML solutions
- Assessing data readiness
- Managing incorrect results
- Define business success criteria
- Key results
- Assessing ML solution readiness
- Defining the input (features) and predicted output format
- Defining output use
- Defining problem type (classification, regression, clustering, etc.)
- Defining outcome of model predictions
- Success metrics
- Determination of when a model is deemed unsuccessful
- Aligning with Google AI principles and practices (e.g. different biases)
- Identifying data sources
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Define ML problem
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Overfitting
- Choice of framework and model
- Model performance against baselines, simpler models, and across the time dimension
- Modeling techniques given interpretability requirements
- Model explainability on Cloud AI Platform
- Transfer learning
- Unit tests for model training and serving
- Hardware accelerators
- Tracking metrics during training
- Model generalization
- Retraining/redeployment evaluation
- Productionizing
- Scale model training and serving
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Build a model
- Training a model as a job in different environments
- Distributed training
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