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Build & Operationalize Data Processing Systems

• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;

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

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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:

• Ingesting appropriate data
• Continuous evaluation
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• 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)
• Leveraging pre-built ML models as a service
• ML APIs (e.g., Vision API, Speech API)
• Impact of dependencies of machine learning models
• Hardware accelerators (e.g., GPU, TPU)
• Distributed vs. single machine
• Operationalizing machine learning models
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Use of edge compute
• Deploying an ML pipeline
• Measuring, monitoring, and troubleshooting machine learning models

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