LetMeRead net Sybex Official Google Cloud Certified Professional Data Engineer Study Guide 1st edition by Dan Sullivan ISBN 1119618436 9781119618454

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Product details:

ISBN 10: 1119618436
ISBN 13: 9781119618454
Author: Dan Sullivan

The proven Study Guide that prepares you for this new Google Cloud exam

The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. 

Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. 

•    Build and operationalize storage systems, pipelines, and compute infrastructure

•    Understand machine learning models and learn how to select pre-built models

•    Monitor and troubleshoot machine learning models

•    Design analytics and machine learning applications that are secure, scalable, and highly available. 

This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

LetMeRead net Sybex Official Google Cloud Certified Professional Data Engineer Study Guide 1st Table of contents:

Appendix Answers to Review Questions

Chapter 1: Selecting Appropriate Storage Technologies

Chapter 2: Building and Operationalizing Storage Systems

Chapter 3: Designing Data Pipelines

Chapter 4: Designing a Data Processing Solution

Chapter 5: Building and Operationalizing Processing Infrastructure

Chapter 6: Designing for Security and Compliance

Chapter 7: Designing Databases for Reliability, Scalability, and Availability

Chapter 8: Understanding Data Operations for Flexibility and Portability

Chapter 9: Deploying Machine Learning Pipelines

Chapter 10: Choosing Training and Serving Infrastructure

Chapter 11: Measuring, Monitoring, and Troubleshooting Machine Learning Models

Chapter 12: Leveraging Prebuilt Models as a Service

Index

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