Google Google Cloud Certified Professional-Data-Engineer-JPN

Google Google Cloud Certified Professional-Data-Engineer-JPN Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Exams

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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

• Deploying an ML pipeline
• Operationalizing machine learning models
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Hardware accelerators (e.g., GPU, TPU)
• ML APIs (e.g., Vision API, Speech API)
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Ingesting appropriate data
• Continuous evaluation
• Leveraging pre-built ML models as a service
• Common sources of error (e.g., assumptions about data)
• Impact of dependencies of machine learning models
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Measuring, monitoring, and troubleshooting machine learning models
• Choosing the appropriate training and serving infrastructure
• Conversational experiences (e.g., Dialogflow)
• Distributed vs. single machine
• Use of edge compute

Reference: https://cloud.google.com/certification/data-engineer

Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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