Google Cloud Platform Big Data and Machine Learning Fundamentals
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).
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.
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
Where to go from here
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