This Google Cloud Platform Big Data and Machine Learning Fundamentals course introduces students 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.

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

1 day

Introducing Google Cloud Platform

• Google Platform Fundamentals Overview.
• Google Cloud Platform Big Data Products.

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.

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.

Scaling Data Analysis

• Fast random access.
• Datalab.
• BigQuery.
• Lab: Build machine learning dataset.

Machine Learning

• Machine Learning with TensorFlow.
• Lab: Carry out ML with TensorFlow
• Pre-built models for common needs.
• Lab: Employ ML APIs.

Data Processing Architectures

• Message-oriented architectures with Pub/Sub.
• Creating pipelines with Dataflow.
• Reference architecture for real-time and batch data processing.

Summary

• Why GCP?
• Where to go from here
• Additional Resources