Google CloudGCP100B

Google Cloud Platform Big Data and Machine Learning Fundamentals

1 day(s) / 7h

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

  • 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

Target audience

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

Prerequisites

  • 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

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

Our training sessions

Place of training :
Dates
03/01/23 Distance Register
07/02/23 Distance Register
07/03/23 Distance Register
04/04/23 Distance Register
02/05/23 Distance Register
06/06/23 Distance Register
04/07/23 Distance Register
01/08/23 Distance Register
05/09/23 Distance Register
03/10/23 Distance Register
07/11/23 Distance Register

Ce cours vous intéresse ?

Place of training :
Dates
03/01/23 Distance Register
07/02/23 Distance Register
07/03/23 Distance Register
04/04/23 Distance Register
02/05/23 Distance Register
06/06/23 Distance Register
04/07/23 Distance Register
01/08/23 Distance Register
05/09/23 Distance Register
03/10/23 Distance Register
07/11/23 Distance Register
1
Inter : 700 € HT / user

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