Google Cloud Fundamentals: Big Data and Machine Learning

550.00 +Iva

Dias: 1
Duração: 7 horas
Próxima Data: 18/03/2022
Área: Machine Learning
Certificação Associada: 

This course is part of the following Certifications:

Google Cloud Certified Professional Machine Learning Engineer (PMLE)
Google Cloud Certified Professional Data Engineer (PDE)

Local: Lisboa e Porto

*Curso disponível em Live Training

Quero inscrever-me
REF: GCF-BDM Categoria: Etiqueta:


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.


  • 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.


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).
Cloud Shell.
Lab: Set up an 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.
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


  • 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.

Outras datas