Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Influencing unstructured data using ML APIs on Cloud Dataproc
• Enable insights from streaming data
• Designing and building data processing systems on the Google Cloud Platform
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Predicting machine models using TensorFlow and Cloud ML

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

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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

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