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

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

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This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Data Processing Architectures
• Additional Resources
• Introducing Google Cloud Platform ‘
• Data Analytics on the Cloud
• Compute and Storage Fundamentals
• Scaling Data Analytics
• Machine Learning

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