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.

The training will be given in 2 half days.

Course overview

Target audience:

• 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

Objectives:

• 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

Content

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