Google Cloud Professional Data Engineer: Get Certified 2020
Build scalable, reliable data pipelines, databases, and machine learning applications.
What you’ll learn
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How to pass the Google Cloud Professional Data Engineer Exam
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Build scalable, reliable data pipelines
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Choose appropriate storage systems, including relational, NoSQL and analytical databases
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Apply multiple types of machine learning techniques to different use cases
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Deploy machine learning models in production
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Monitor data pipelines and machine learning models
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Design scalable, resilient distributed data intensive applications
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Migrate data warehouse from on-premises to Google Cloud
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Evaluate and improve the quality of machine learning models
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Grasp fundamental concepts in machine learning, such as backpropagation, feature engineering, overfitting and underfitting.
Requirements
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Understanding of basic cloud computing concepts such as virtual machines and databases.
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One year or more experience working with data management or data analysis
Who this course is for:
- Cloud engineers and architects who want to pass the Professional Data Engineer exam
- Data engineers who want to learn about Google’s advanced tools and services for data engineering
- Data scientists and data engineers who want to understand machine learning concepts
- Cloud application developers who want to use machine learning to build applications
- Devops engineers who need to support data engineering pipelines and machine learning models
