Prerequisites
The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.
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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:
- Testing for target performance
- Hooking into model and dataset versioning
- Model/dataset lineage
- A/B and canary testing
- Organization and tracking experiments and pipeline runs
- Identification of components, parameters, triggers, and compute needs
- Hybrid or multi-cloud strategies
- Track and audit metadata
- Model binary options
- Constructing and testing of parameterized pipeline definition in SDK
- Use CI/CD to test and deploy models
- Implement serving pipeline
- Orchestration framework
- Decoupling components with Cloud Build
- Tuning compute performance
- Performing data validation
- Storing data and generated artifacts
- Setup of trigger and pipeline schedule
- Google Cloud serving options
- Hooking models into existing CI/CD deployment system
- Implement training pipeline
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Data preparation and processing | - Data ingestion and pipelines
- 1. Build data pipelines for training and serving
- 2. Use BigQuery and data processing services
- Feature engineering
- 1. Feature selection and representation techniques
- 2. Transform and preprocess datasets
|
| Topic 2: Deployment and operations | - Model deployment
- 1. Batch and online prediction systems
- 2. Deploy models using Vertex AI endpoints
- Monitoring and maintenance
- 1. Monitor model drift and performance
- 2. Retraining and lifecycle management
|
| Topic 3: Designing ML solutions | - Framing ML problems
- 1. Translate business problems into ML tasks
- 2. Define success metrics and evaluation criteria
- ML architecture design
- 1. Select appropriate ML models and approaches
- 2. Design scalable ML systems on GCP
|
| Topic 4: ML pipeline automation and orchestration | - Pipeline design
- 1. Build end-to-end ML pipelines
- 2. Use Vertex AI Pipelines
|
| Topic 5: ML model development | - Evaluation
- 1. Model validation strategies
- 2. Evaluate model performance metrics
- Model training and tuning
- 1. Train models using TensorFlow / Vertex AI
- 2. Hyperparameter tuning and optimization
|