Google Cloud

Google Cloud Big Data and Machine Learning Fundamentals

Duration: 1 day

Industry: Information Technology

About Google Machine Learning

Google Cloud Big Data and Machine Learning Fundamentals introduce participants to the capabilities of the Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud 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.

For more information about Google's Big Data, please check this page.

Who can benefit?


  • Data analysts, Data scientists, Business analysts getting started with Google Cloud
  • 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 for use by data scientists

Google Machine Learning - what you'll learn

  • Identify the purpose and value of the key Big Data and Machine Learning products on Google Cloud
  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis

     Course Outline

  • Google Platform Fundamentals Overview
  • Google Cloud Platform Big Data Products
  • CPUs on demand (Compute Engine)
  • A global filesystem (Cloud Storage)
  • CloudShell
  • Lab: Set up a Ingest-Transform-Publish data processing pipeline
  • 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
  • Fast random access
  • Datalab
  • BigQuery
  • Lab: Build machine learning dataset
  • Machine Learning with TensorFlow
  • Lab: Carry out ML with TensorFlow
  • Pre-built models for common needs
  • Lab: Employ ML APIs
  • Message-oriented architectures with Pub/Sub
  • Creating pipelines with Dataflow
  • Reference architecture for real-time and batch data processing
  • Why GCP?
  • Where to go from here
  • Additional Resources
  • Train and use a neural network using TensorFlow
  • Employ ML APIs
  • Choose between different data processing products on Google Cloud

 

  • 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

Contact P2L to schedule the dates for this course.