Google Cloud Platform Big Data and Machine Learning Fundamentals

1 day (7 hours)

Course overview

This 1 day course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

Learning outcomes

This course teaches participants the following skills:

  • Knowledge of Google Cloud Platform products and services, particularly those related to data processing and machine learning
  • Knowledge of basic products and services related to computing and storage
  • Knowledge of Cloud SQL and Dataproc
  • Knowledge of Datalab and BigQuery
  • Knowledge of TensorFlow and Machine Learning APIs
  • Knowledge of Pub / Sub and Dataflow


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

  • experience with a common query language such as SQL
  • experience with an ETL
  • data modeling experience
  • experience in machine learning and / or statistics
  • experience with programming in Python

Target audience

This course is intended for the following participants:

Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python

Course Outline

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

Module 1: Introducing Google Cloud Platform

Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products.

Module 2: Compute and Storage Fundamentals

CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline.

Module 3: Data Analytics on the Cloud

Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc.

Module 4: Scaling Data Analysis

Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset.

Module 5: Machine Learning

Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs.

Module 6: Data Processing Architectures

Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Module 7: Summary

Why GCP? Where to go from here Additional Resources

€700 ex. VAT

Contact us

You can unsubscribe from our communications at any time.

In order to take into account your request, we must store and process your personal data. If you authorize us to store your personal data for this purpose, check the box below.

By clicking on « Send » below, you authorize SFEIR to store and process the personal data submitted above so that it can provide you with the requested content.