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The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

The following will be discussed here:

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

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

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