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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:

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

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

• Batch and streaming
• Building and operationalizing storage systems
• Lifecycle management of data
• Data cleansing
• Building and operationalizing data processing systems
• Validating a migration
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Integrating with new data sources
• Provisioning resources
• Data acquisition and import
• Building and operationalizing pipelines
• Awareness of current state and how to migrate a design to a future state
• Adjusting pipelines
• Building and operationalizing processing infrastructure
• Transformation
• Storage costs and performance
• Testing and quality control
• Monitoring pipelines
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)

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