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Google Cloud Fundamentals: Big Data
and Machine Learning - GCF-BDM
Version 2024

Logo GCP.png

Résumé

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.

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Contenu du cours

Module 1 : Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview

  • Google Cloud Platform Data Products and Technology

  • Usage scenarios

  • Lab: Sign up for Google Cloud Platform

 

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

  • CloudSQL: your SQL database on the cloud

  • Lab: Importing data into CloudSQL and running queries

  • Spark on Dataproc

  • Lab: Machine Learning Recommendations with SparkML

 

Module 4 : Scaling Data Analysis

  • Fast random access

  • Datalab

  • BigQuery

  • Lab: Build machine learning dataset

  • Machine Learning with TensorFlow

  • Lab: Train and use neural network

  • Fully built models for common needs

  • Lab: Employ ML APIs

 

Module 5 : Data Processing Architectures

  • Message-oriented architectures with Pub/Sub

  • Creating pipelines with Dataflow

  • Reference architecture for real-time and batch data processing

 

Module 6 : Summary

  • Why GCP

  • Where to go from here

  • Additional Resources

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Délai d’accès 

Se référer aux dates figurant au planning

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Évaluations et sanctions de la formation

  • Quizz intermédiaires 

  • Lab technique en fin de module

  • Évaluation de satisfaction via un questionnaire pré formation, à chaud et à froid

  • Attestation de présence et de formation 

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* Formation distanciel possible :

  • de votre entreprise

  • de chez vous

  • de nos locaux à Sophia Antipolis (équipement Cisco Webex Board)

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Nos formations sont accessibles aux personnes en situation de handicap.

Un questionnaire envoyé en amont de la formation invite les participants à nous contacter s’ils ont besoins d’aménagements spécifiques en lien avec leur situation de handicap. Nous nous employons à rechercher, avec les personnes concernées, les moyens de compensation qui leur seront adaptés.

Pour en valider l'accès merci de nous contacter contact@formation-IT.org

Durée 

1 jour soit 7 heures

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Prix public

750 € HT

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Dates

à Paris ou distanciel*​

  • sur demande

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Public concerné

This class is intended for the following participants:

  • Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform

  • 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 Platform for use by data scientists

 

Objectifs

This course teaches participants the following skills :

  • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform

  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform

  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis

  • Train and use a neural network using TensorFlow

  • Employ ML APIs

  • Choose between different data processing products on the Google Cloud Platform

 

Pré requis 

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

  • Basic proficiency with common query language such as SQL

  • Experience with data modeling, extract, transform, load activities

  • Developing applications using a common programming language such Python

  • Familiarity with Machine Learning and/or statistics

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Méthodes et moyens pédagogiques

Une pédagogie basée sur l'alternance de phases théoriques, d'ateliers de mise en pratique, de retours d'expérience et d'échanges.​

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📌 date confirmée

💻 distanciel

🆓 session gratuite

🇬🇧  langue anglaise

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