Google Cloud Certified Professional-Data-Engineer

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Operationalize ML Models

• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;

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Professional-Data-Engineer exam certification is one of the most important certification recently. When qualified by the Professional-Data-Engineer certification, you will get a good job easily with high salary. Besides, the career opportunities will be open for a certified person. Now, you can get the valid and best useful Professional-Data-Engineer exam training material. Professional-Data-Engineer will help you to strengthen your technical knowledge and allow you pass at your first try.

Google Professional-Data-Engineer Exam Syllabus Topics:

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