We provide course about Google Cloud Fundamentals: Big Data and Machine Learning. This 1-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform.
Content:
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.
Learning objectives:
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
Course outline:
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 an 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 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
Target audience:
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
Prerequisites:
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 as Python
• Familiarity with Machine Learning and / or statistics