The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- 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
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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.
Google Professional-Machine-Learning-Engineer certification exam is experiencing a great demand within the IT industry. In recent years, Google Professional-Machine-Learning-Engineer certificate has become a global standard for many successful IT companies.
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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:
- Managing large samples (TFRecords)
- Streaming data (e.g. from IoT devices)
- Transformations (TensorFlow Transform)
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Feature selection
- Data ingestion
- Batching and streaming data pipelines at scale
- Data leakage and augmentation
- Evaluation of data quality and feasibility
- Feature engineering
- Visualization
- Statistical fundamentals at scale
- Build data pipelines
- Handling missing data
- Database migration
- Monitoring/changing deployed pipelines
- Encoding structured data types
- Design data pipelines
- Data privacy and compliance
- Feature crosses
- Data exploration (EDA)
- Handling outliers
- Class imbalance
- Data validation
Difficulty in Writing Professional Machine Learning Engineer - Google
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