Operationalize ML Models

• 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.
• 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;
• 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);
• 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;

Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

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

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• Creating a Data Lake
• Introduction to Building Batch Data Pipelines
• Advanced BigQuery Performance and Functionality
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Prebuilt ML Models APIs for Unsaturated Data
• Building a Data Warehouse
• Introduction to Data Engineering
• Big Data Analytics with Cloud Al Platform Notebook
• Cloud Dataflow Streaming Features
• Serverless Messaging Using Cloud Sub/Pub
• Custom Model building Using SQL in BigQuery ML
• Bigtable Streaming Features and High-Throughput BigQuery
• Custom Model building Utilizing Cloud AutoML
• Performing Spark on Cloud Dataproc
• Introduction to Processing Streaming Data
• Production ML Pipelines and use of Kubeflow
• Serverless Data Processing with Cloud Dataflow

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