Google Cloud Fundamentals: Big Data and Machine Learning Training

To get the most of out of this course, participants should have:

• 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.

• 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.

Session 1

Welcome

1. Facility Logistics

1 Introduction to Google Cloud Platform

• Google Cloud Platform infrastructure and big data products
• Demo: BigQuery Github query
• The different data roles in an organization
• What you can do with GCP
• Activity: Explore a customer use case
• Lab 1: Exploring a Public Dataset with BigQuery

2 Product Recommendations using Cloud SQL and Spark

• Compare Google Cloud Big Data products and services
• Managed Hadoop in the cloud
• Demo: Creating a Cluster
• Your SQL database in the cloud
• Lab 2: Product Recommendation using Cloud SQL and Spark

3 Predicting Visitor Purchases using BigQuery Machine Learning

• Introduction to BigQuery
• Fast SQL Query Engine
• Managed Storage for Datasets
• Demo: Google Sheets to BQ
• Insights from Geographic data
• Demo: BigQuery ML
• Creating ML models with SQL w/BigQuery ML
• Lab 3: Predicting Visitor Purchases BigQuery ML

Session 2

4 Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio

• Introduction
• Message-oriented architectures
• Serverless data pipelines
• Data Visualization w/Data Studio
• Lab 4: Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio

5 Deriving Insights from Unstructured Data using Machine Learning

• Introduction to Machine Learning
• Pre-built ML models
• Demo: Cloud Vision API
• Codeless ML with AutoML
• Lab 5: Classify Images using AutoML

6 Summary

• Recap of lesson