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Career Bonuses
The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Preparation Process
The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
| ML model development | - Evaluation
- 1. Evaluate model performance metrics
- 2. Model validation strategies
- Model training and tuning
- 1. Train models using TensorFlow / Vertex AI
- 2. Hyperparameter tuning and optimization
|
| Data preparation and processing | - Feature engineering
- 1. Transform and preprocess datasets
- 2. Feature selection and representation techniques
- Data ingestion and pipelines
- 1. Use BigQuery and data processing services
- 2. Build data pipelines for training and serving
|
| Deployment and operations | - Monitoring and maintenance
- 1. Monitor model drift and performance
- 2. Retraining and lifecycle management
- Model deployment
- 1. Batch and online prediction systems
- 2. Deploy models using Vertex AI endpoints
|
| ML pipeline automation and orchestration | - Pipeline design
- 1. Use Vertex AI Pipelines
- 2. Build end-to-end ML pipelines
|
| Designing ML solutions | - ML architecture design
- 1. Select appropriate ML models and approaches
- 2. Design scalable ML systems on GCP
- Framing ML problems
- 1. Define success metrics and evaluation criteria
- 2. Translate business problems into ML tasks
|