Google Cloud Big Data and Machine Learning Fundamentals

(1 day)


This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Course Objectives

  • Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
  • Design streaming pipelines with Dataflow and Pub/Sub.
  • Analyze big data at scale with BigQuery.
  • Identify different options to build machine learning solutions on Google Cloud.
  • Describe a machine learning workflow and the key steps with Vertex AI.
  • Build a machine learning pipeline using AutoML.


  • Data analysts, data scientists, and business analysts who are getting started with Google Cloud
  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports
  • Executives and IT decision makers evaluating Google Cloud for use by data scientists


Basic understanding of one or more of the following:

  • Database query language such as SQL
  • Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
  • Machine learning models such as supervised versus unsupervised models

Course Outline

The course includes presentations, demonstrations, and hands-on labs.

Module 1: Big Data and Machine Learning on Google Cloud

  • Identify the different aspects of Google Cloud’s infrastructure
  • Identify the big data and machine learning products on Google Cloud

Module 2: Data Engineering for Streaming Data

  • Describe an end-to-end streaming data workflow from ingestion to data visualization
  • Identify modern data pipeline challenges and how to solve them at scale with Dataflow
  • Build collaborative real-time dashboards with data visualization tools

Module 3: Big Data with BigQuery

  • Describe the essentials of BigQuery as a data warehouse
  • Explain how BigQuery processes queries and stores data
  • Define BigQuery ML project phases
  • Build a custom machine learning model with BigQuery ML

Module 4: Machine Learning Options on Google Cloud

  • Identify different options to build ML models on Google Cloud
  • Define Vertex AI and its major features and benefits
  • Describe AI solutions in both horizontal and vertical markets

Module 5: The Machine Learning Workflow with Vertex AI

  • Describe a ML workflow and the key steps
  • Identify the tools and products to support each stage
  • Build an end-to-end ML workflow using AutoML

Module 6: Summary

  • Recap of key learning points
  • Resources