en
fr
IT Training

Google Cloud Fundamentals: Big Data and Machine Learning

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.

Who should attend this course?

This class is intended for the following:

  • 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
Prerequisites

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

This course teaches participants the following skills:

  • 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.

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.
  • Lab: Exploring a BigQuery Public Dataset

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.
  • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

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.
  • Lab: Predicting Visitor Purchases Using 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.
  • Lab: Vertex AI: Predicting Loan Risk with AutoML

Module 5: Course Summary

  • 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.

Practical information

Duration

1 Day

Languages

EN

Price

€ 695 + 3% VAT

Location

Virtual Class

Schedule

Guaranteed to run

Sessions in English
12/03/2025Book
16/05/2025Book
08/09/2025Book
01/12/2025Book
Sessions in Dutch
Contact us for more infoBook
Sessions in French
Contact us for more infoBook

Share this course on

Book your training

Enter your information to confirm your booking.

    You might also be interested in

    IT Training
    ONTAP Troubleshooting
    04-07/03/2025, 03-06/06/2025 ...
    In this Partner-only course, you increase your proficiency as a NetApp® ONTAP® software engineer by learning how to troubleshoot ONTAP…

    Prerequisite test

    Take the test

    Looking for a tailor made solution?

    Get started with personal training