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Professional Machine Learning Engineer - Google Certified salary
The estimated average salary of Professional Machine Learning Engineer - Google is listed below:
- United States: 114,000 USD
- India: 8,580,000 INR
- Europe: 97,000 EURO
- England: 87,200 POUND
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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:
- Identifying data sources
- Defining business problems
- Identifying nonML solutions
- Defining problem type (classification, regression, clustering, etc.)
- Success metrics
- Defining outcome of model predictions
- Managing incorrect results
- Aligning with Google AI principles and practices (e.g. different biases)
- Determination of when a model is deemed unsuccessful
- Define ML problem
- Defining the input (features) and predicted output format
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Assessing ML solution readiness
- Assessing data readiness
- Define business success criteria
- Defining output use
- Assessing and communicating business impact
- Key results
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Feature selection
- Data privacy and compliance
- Data ingestion
- Handling outliers
- Data leakage and augmentation
- Handling missing data
- Batching and streaming data pipelines at scale
- Transformations (TensorFlow Transform)
- Design data pipelines
- Feature engineering
- Data validation
- Database migration
- Statistical fundamentals at scale
- Managing large samples (TFRecords)
- Feature crosses
- Visualization
- Class imbalance
- Encoding structured data types
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Evaluation of data quality and feasibility
- Build data pipelines
- Monitoring/changing deployed pipelines
- Data exploration (EDA)
- Streaming data (e.g. from IoT devices)
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deployment and operations | - Monitoring and maintenance
|
| Topic 2: Data preparation and processing | - Feature engineering
|
| Topic 3: Designing ML solutions | - ML architecture design
|
| Topic 4: ML model development | - Model training and tuning
|
| Topic 5: ML pipeline automation and orchestration | - Pipeline design
|




