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Google Cloud Platform Fundamentals: Big Data & Machine Learning

Want to understand the big data capabilities of the Google Cloud Platform? This one-day course is the first step towards Google machine learning certification.
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1 day course
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Classroom, Virtual, Private
Classroom
Face to face, interactive classroom training run from our global training centres.
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.
Private
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

As a Google Cloud Partner, we’ll help equip you with the skills to process data efficiently and effectively to scale, using a range of specially designed Google Products on the Google Cloud Platform.

Jellyfish has been selected by Google to facilitate the delivery of this one-day instructor led course. All of our trainers are experienced practitioners, so you can learn with total confidence.

Through expert guidance and practical labs, you’ll gain 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.

This Google Cloud Platform Fundamentals: Big Data & Machine Learning course is part of the Professional Data Engineer track and is available at our training centre in The Shard, London. We also offer private training at a location of your choice or via Virtual Classroom.

Course overview
Who should attend:
This course 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, visualising query results and creating reports
  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists
Walk away with the ability to:
  • 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
Prerequisites
To get the most of out of this course, you 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
Course agenda
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
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