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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:

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

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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Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

• India - 25,42,327 INR
• Europe - 135,347 EURO
• United State - 151,247 USD
• England - 115,632 POUND

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

Google Professional-Data-Engineer日本語 Exam Syllabus Topics: