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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:

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

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

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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