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  • Book Overview & Buying Journey to Become a Google Cloud Machine Learning Engineer
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

By : Dr. Logan Song
5 (61)
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

5 (61)
By: Dr. Logan Song

Overview of this book

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.
Table of Contents (23 chapters)
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1
Part 1: Starting with GCP and Python
4
Part 2: Introducing Machine Learning
8
Part 3: Mastering ML in GCP
13
Part 4: Accomplishing GCP ML Certification
15
Part 5: Appendices
2
Appendix 2: Practicing Using the Python Data Libraries

ML data storage and processing

As we discussed in Chapter 4, Developing and Deploying ML Models, storing data involves collecting raw data from various data sources and storing it in a centralized repository. On the other hand, data processing includes both data engineering and feature engineering. Data engineering is the process of converting raw data (the data in its source form) into prepared data (the dataset in the form that is ready to be input into ML tasks). Feature engineering then tunes the prepared data to create the features expected by the ML model.

For structured data, we recommend using Google Cloud BQ to store and process it. For unstructured data, videos, audio, and image data, we recommend using Google Cloud object storage to store them and Google Cloud Dataflow or Dataproc to process them. As we have discussed, Dataflow is a managed service that uses the Apache Beam programming model to convert unstructured data into binary formats and can improve data ingestion...

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