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Google Cloud Big Data and Machine Learning Fundamentals

SS Course: 56024

Course Overview

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.                                                                  

Scheduled Classes

07/15/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

Identify 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.
  • This course teaches participants the following skills:


Viewing outline for:
Recognize the data-to-AI lifecycle on Google Cloud
  • Identify the connection between data engineering and machine learning
  • Identify the different aspects of Google Clouds infrastructure.
  • Identify the big data and machine learning products on Google Cloud.
  • Lab: Exploring a BigQuery Public Dataset
  • Quiz
  • 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.
  • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
  • Quiz
  • 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.
  • Lab: Predicting Visitor Purchases Using BigQuery ML
  • Quiz
  • 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.
  • Quiz
  • 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.
  • Lab: Vertex AI: Predicting Loan Risk with AutoML
  • Quiz
  • This section reviews the topics covered in the course and provides additional resources for further learning.
  • Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.


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
  • To get the most of out of this course, participants should have:

    Who Should Attend

    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
    • This class is intended for the following:

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