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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A practical guide to operationalizing scalable data analytics systems on GCP

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800561328
Length 440 pages
Edition 1st Edition
Languages
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Author (1):
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Adi Wijaya Adi Wijaya
Author Profile Icon Adi Wijaya
Adi Wijaya
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering FREE CHAPTER 3. Chapter 2: Big Data Capabilities on GCP 4. Section 2: Building Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Orchestration for Batch Data Loading Using Cloud Composer 7. Chapter 5: Building a Data Lake Using Dataproc 8. Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow 9. Chapter 7: Visualizing Data for Making Data-Driven Decisions with Data Studio 10. Chapter 8: Building Machine Learning Solutions on Google Cloud Platform 11. Section 3: Key Strategies for Architecting Top-Notch Data Pipelines
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Cost Strategy in GCP 14. Chapter 11: CI/CD on Google Cloud Platform for Data Engineers 15. Chapter 12: Boosting Your Confidence as a Data Engineer 16. Other Books You May Enjoy

Chapter 8: Building Machine Learning Solutions on Google Cloud Platform

The first machine learning (ML) solution came from the 1950s era. And I believe most of you know that in recent years, it's become very popular. It's undeniable that the discussion of artificial intelligence (AI) and ML is one of the hottest topics in the 21st century. There are two main drivers of this. One is the advancement in the infrastructure, while the second is data. This second driver brings us, as data engineers, into the ML area. 

In my experience discussing ML with data engineers, there are two different reactions – either very excited or totally against it. Before you lose interest in finishing this chapter, I want to be clear about what we are going to cover. 

We are not going to learn about ML from any historical stories nor the mathematical aspects of it. Instead, I am going to prepare you, as data engineers, for potential ML involvement in your GCP environment....

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