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

The following will be discussed here:

• Hardware accelerators (e.g., GPU, TPU)
• Impact of dependencies of machine learning models
• Ingesting appropriate data
• Use of edge compute
• Leveraging pre-built ML models as a service
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Distributed vs. single machine
• ML APIs (e.g., Vision API, Speech API)
• Continuous evaluation
• Deploying an ML pipeline
• Choosing the appropriate training and serving infrastructure
• Conversational experiences (e.g., Dialogflow)
• Common sources of error (e.g., assumptions about data)
• 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)
• Operationalizing machine learning models
• Measuring, monitoring, and troubleshooting machine learning models

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

Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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