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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- England: 87,200 POUND
- United States: 114,000 USD
- India: 8,580,000 INR
- Europe: 97,000 EURO
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.
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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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
- Organization and tracking experiments and pipeline runs
- Hooking into model and dataset versioning
- Testing for target performance
- Model/dataset lineage
- Google Cloud serving options
- Setup of trigger and pipeline schedule
- Implement training pipeline
- Track and audit metadata
- Hybrid or multi-cloud strategies
- Hooking models into existing CI/CD deployment system
- Performing data validation
- Identification of components, parameters, triggers, and compute needs
- Model binary options
- Orchestration framework
- Constructing and testing of parameterized pipeline definition in SDK
- Storing data and generated artifacts
- Decoupling components with Cloud Build
- Tuning compute performance
- A/B and canary testing
- Implement serving pipeline
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer