Google Cloud Platform Big Data and Machine Learning Fundamentals

Course Length: 1 Day

Delivered: Virtually

OVERVIEW:

This 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.

COURSE PREREQUISITES:

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

TARGET AUDIENCE:

This class is intended for the following:

• 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.

COURSE OBJECTIVES:

This course teaches students 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 CONTENT:

1 - Introducing Google Cloud Platform

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

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.

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.

4 - Scaling Data Analysis

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

5 - Machine Learning

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

6 - Data Processing Architectures

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

7 - Summary

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