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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
- Hardware accelerators
- Modeling techniques given interpretability requirements
- Build a model
- Model generalization
- Retraining/redeployment evaluation
- Model performance against baselines, simpler models, and across the time dimension
- Choice of framework and model
- Training a model as a job in different environments
- Unit tests for model training and serving
- Transfer learning
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Model explainability on Cloud AI Platform
- Scale model training and serving
- Distributed training
- Productionizing
- Tracking metrics during training
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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
- India: 8,580,000 INR
- Europe: 97,000 EURO
- United States: 114,000 USD
Who should take the Professional Machine Learning Engineer - Google
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.
The Google Professional-Machine-Learning-Engineer exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.
The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Architect low-code AI solutions | 12% | - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools |
| Train and deploy models | 20% | - Implement generative AI deployment patterns - Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure |
| Scale prototypes into AI models | 18% | - Select appropriate model architectures and frameworks - Optimize model performance and generalization - Design and run experiments - Work with foundation models and generative AI techniques |
| Monitor and optimize AI solutions | 16% | - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health |
| Automate and orchestrate ML pipelines | 18% | - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows - Automate retraining and model updates |
