This 1 day course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
This course teaches participants the following skills:
- Knowledge of Google Cloud Platform products and services, particularly those related to data processing and machine learning
- Knowledge of basic products and services related to computing and storage
- Knowledge of Cloud SQL and Dataproc
- Knowledge of Datalab and BigQuery
- Knowledge of TensorFlow and Machine Learning APIs
- Knowledge of Pub / Sub and Dataflow
To get the most out of this course, participants should have:
- experience with a common query language such as SQL
- experience with an ETL
- data modeling experience
- experience in machine learning and / or statistics
- experience with programming in Python
This course is intended for the following participants:
Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python
The course includes presentations, demonstrations, and hands-on labs.
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