Google Cloud Big Data and Machine Learning Fundamentals Training

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 Details


1 day


  • 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

Target Audience

  • 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

Skills Gained

  • 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.
Course Outline
  • Introduction
    • Recognize the data-to-AI lifecycle on Google Cloud
    • Identify the connection between data engineering and machine learning
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Conclusion
Upcoming Course Dates
USD $900
Online Virtual Class
Date: Jul 15, 2024
Time: 9 AM - 6 PM ET
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